Data at Brex

The Data organization develops insights, models, and data infrastructure for teams across Brex, including Sales, Marketing, Product, Engineering, and Operations. Our Data Scientists, Analysts, and Engineers work together to make data—and insights derived from data—a core asset across the company.

What you’ll do

As a senior Data Analyst (DA III), you will own the end-to-end analytics lifecycle for one or more business areas at Brex. You’ll go beyond building dashboards—you’ll frame the right questions, design rigorous analyses, apply statistical methods, and translate your findings into clear recommendations for leadership. You will also serve as a technical leader on the Data Analytics team, mentoring more junior analysts and helping define the standards and best practices that elevate the team’s work.

This role sits at the intersection of analytics, analytics engineering, and business strategy. You’ll work in a modern data stack environment and partner closely with Data Scientists, Data Engineers, and senior leaders across the organization.

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Own the analytics lifecycle for assigned business areas: from problem framing and data sourcing through analysis, insight generation, and stakeholder presentation.
• Build and maintain dashboards and self-service reporting tools that enable business teams to independently track performance, identify risks, and make data-driven decisions.
• Write production-quality SQL and Python code to extract, transform, and analyze data at scale.
• Collaborate with Data Engineers and Data Scientists to develop and maintain analytical data models, improve data pipelines, and ensure data quality across the organization.
• Partner with leadership across Sales, Operations, Product, Finance, and other departments to identify high-impact analytical opportunities and deliver actionable recommendations.
• Mentor other data analysts and contribute to the development of team standards, documentation, code review practices, and analytical frameworks.
• Proactively identify gaps in data infrastructure, propose improvements, and contribute to the evolution of the team’s tooling and processes.

Requirements

• 5+ years of experience in data analytics, business intelligence, or a related quantitative role.
• 3+ years of experience partnering directly with Sales, Operations, Product, or equivalent business teams as an embedded analytics partner.
• Advanced SQL proficiency, including CTEs, window functions, performance optimization, and working across complex data models.
• Proficiency in Python for data analysis, automation, and modeling (Pandas, NumPy, scikit-learn, or similar).
• Experience with cloud data warehouses, particularly Snowflake (BigQuery and Databricks also valued).
• Hands-on experience with BI and data visualization tools (Looker, Tableau, Hex, or similar).
• Strong stakeholder management skills—proven ability to present complex technical findings to non-technical audiences.
• Experience with generative AI and LLM-based tools (Claude Code, Cursor, GitHub Copilot) to perform and accelerate analyses, automated reporting, and build self-service data tools.

Bonus points

• Demonstrated experience applying statistical methods to business problems (e.g., regression, classification, A/B testing).
• Experience with dbt for data modeling and transformation.
• Experience building and maintaining data pipelines using orchestration tools such as Airflow.
• Experience working with APIs for data ingestion and integration.
• Familiarity with version control systems (Git).
• Experience in fintech, financial services, or payments.
• Track record of leading cross-functional analytics projects from scoping through delivery.

Compensation

The expected salary range for this role is $114,192 - $142,740. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank (the “Bank”) is seeking a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale, with a strong emphasis on Large Language Model (LLM) applications and modern MLOps & AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of the IT Application Development and Support team and working closely with the Chief Artificial Intelligence Officer, transforming innovative AI prototypes into robust, scalable production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities (e.g., generative AI systems, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Production Architecture & AIOps

• Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services.
• Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management.
• Design and maintain scalable software applications with integrated AI/ML capabilities.
• Develop software architecture and design patterns to ensure performance and scalability.
• Write clean, maintainable code in general-purpose programming languages (Python, Java, C, C++, Go).
• Implement and manage data pipelines for preprocessing and transforming data for AI/ML models.
• Integrate AI/ML models into production environments and optimize for reliability and scalability.
• Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions.
• Conduct code reviews and provide technical guidance to junior developers.
• Stay current with advancements in software engineering and AI/ML technologies.
• Adhere to agile and lean software development best practices.
• Thoroughly document all developed models and processes according to relevant policies and standards.
• Support the production environment by either resolving technical or functional issues, in line with the procedures defined by the Bank.

Cross-Functional Collaboration

• Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms.
• Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback.

Scaling & Performance

• Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability.
• Optimize model serving using techniques like model compression, caching, and hardware acceleration.

Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues).

Qualifications & Skills:

• 6+ years of experience
• Master’s or PhD in a relevant field (Computer Science, Software Engineering,  Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML
• LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using RAG, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures.
• MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow).
• Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering seamless user experiences through robust software engineering.
• Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams).
• Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets.
• Strong understanding of AI/ML algorithms, application architecture, and design patterns.
• Excellent problem-solving, analytical, communication, and collaboration skills.

Preferred Qualifications & Skills

• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling.
• Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams.
• Knowledge of SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment.
• Excellent written and verbal communication skills.
• Strong listening and teaching abilities.
• Demonstrated analytical, troubleshooting, and problem-solving skills.
• Quick learner of new technologies.
• Self-directed with strong technology and communication skills.
• Ability to synthesize multiple sources of information and understand the broader operational context of the Bank.
• Collaborative team player with a practical and creative approach in a dynamic work environment.
• Ability to handle ambiguity, multitask, and adapt quickly to changing priorities.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

About This Role

Lead applied ML engineering on Scale's Applied ML team, powering data infrastructure for leading agentic LLMs (ChatGPT, Gemini, Llama). You will build scalable multi-agent systems to validate agentic reasoning and behaviors, scale human expertise, and drive research into real-world agent reliability failures despite strong benchmarks, shipping production fixes.

Ideal for exceptional engineers with deep research rigor and a relentless focus on practical, high-impact systems. You will iterate rapidly with data, leverage AI tools to accelerate development, and collaborate tightly across engineering, product, and research.

If you excel at turning frontier agent research into reliable deployed systems, we want to hear from you.

You will:

• Build and deploy multi-agent systems for agentic reasoning validation
• Develop pipelines to detect errors and scale human judgment
• Combine classical ML, LLMs, and multi-agent techniques for reliability
• Lead research into agent failure modes and ship fixes
• Use AI tools to speed prototyping and iteration
• Build data-driven evaluations and deploy rapid improvements
• Integrate systems into Scale's platform

Ideally You’ll Have: 

• PhD or MSc in Computer Science, Mathematics, Statistics, or related field
• 3+ years shipping scaled production ML systems
• Demonstrated real-world impact
• Mastery of PyTorch, TensorFlow, JAX, or scikit-learn
• Deep expertise in agentic LLMs and multi-agent systems
• Strong software engineering and microservices (AWS/GCP)
• Rapid, data-driven iteration
• Proficiency using AI tools to accelerate work
• Strong research depth with practical bias
• Excellent cross-functional communication

Nice to Have: 

• Experience prototyping agent evaluation/reliability systems
• Human-in-the-loop or annotation pipeline work
• Open-source contributions in agents, evaluation, or alignment
• Publications on agent reliability (NeurIPS, ICML, ICLR)

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As a Senior Value Engineering Consultant specializing in the Energy Sector, you are pushing the envelope in solving business-critical problems for our strategic customers across utilities, oil & gas, and renewable energy. You will be working with our most strategic energy clients, understanding their unique objectives—from grid modernization and asset lifecycle management to navigating the energy transition—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI, and Databricks.
  
  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands our customers’ complex business realities (bridging IT and OT systems) and enables them to industrialize AI, unlocking real ROI on deployments at scale. You will prototype these solutions, demonstrate their value to Energy Executives, and ensure successful implementation, adoption, and value realization in order to increase the footprint of Celonis across the global energy landscape.

Key Responsibilities:

• AI Discovery & Solutioning: Understand energy customers' AI strategy and sector-specific challenges (e.g., predictive maintenance, outage management, supply chain resilience, or regulatory compliance). As a Celonis product and energy domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex operational problems. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points specific to power generation, transmission, and O&G operations.

• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent field service routing or autonomous procurement).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and stringent energy compliance frameworks.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the Energy vertical, scaling knowledge across the organization regarding utilities, upstream/downstream oil & gas, and renewable energy processes.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Energy sector (Utilities, Oil & Gas, and/or Renewables). This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.

• Deep understanding of business processes native to the Energy sector (such as Asset Management, Field Services, Supply Chain, Capital Projects, or Trading) with the ability to translate high-level business needs into specific, impactful AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., automated processing of engineering documents, regulatory filings, or intelligent diagnostic chatbots).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies relevant to handling large-scale industrial data.

• Strong presentation skills to both internal and external stakeholders (including C-level executives and operational leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly regulated industries.

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex) and familiarity with IT/OT convergence and industrial IoT data structures.

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As an Senior Value Engineer specializing in CPG and Fleet Logistics, you are pushing the envelope in solving business-critical problems for some of the world's most recognized consumer brands and global mobility networks. You will be working intimately with our most strategic enterprise clients, understanding their uniquely complex objectives—spanning from high-velocity beverage manufacturing and Direct Store Delivery (DSD) optimization to massive-scale fleet lifecycle management and dynamic asset routing. You will build Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners (e.g., Microsoft, OpenAI, Databricks).

  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the intricate realities of our customers' massive supply chains, vehicle fleets, and distribution networks. This enables them to industrialize AI, unlocking real ROI on deployments at scale—ensuring products are always on the shelf and vehicles are always ready for the customer. There is no AI without PI. You will prototype these solutions, demonstrate their value to global Chief Supply Chain Officers (CSCOs) and Fleet Operations Executives, and ensure successful implementation, adoption, and value realization to solidify and expand the footprint of Celonis across these premier global networks.

Key Responsibilities:

• AI Discovery & Solutioning: Understand the clients' overarching AI strategies and the distinct supply chain challenges inherent to high-volume CPG and fleet mobility (e.g., predictive demand forecasting, route optimization, fleet predictive maintenance, dynamic asset allocation, and mitigating raw material volatility). As a Celonis product and logistics domain expert, translate these complex, physical-world challenges into innovative AI solutions that drive measurable impact.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the expansion/pre-sales cycle within various manufacturing, distribution, and fleet management business units. Remain deeply involved post-sale to guide implementation, ensuring agreed value, efficiency, and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box with a "can-do" attitude, tackling heavily siloed legacy logistics and telematics networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in client hackathons, solving critical pain points across inventory allocation, fleet routing, and DSD execution.

• Agentic Process Transformation: Support these enterprise clients in achieving tangible ROI from AI at scale. Enable a fundamental shift from traditional, rule-based automation to autonomous AI agents empowered by the Celonis Process Intelligence Platform (e.g., autonomous fleet rebalancing, intelligent shipment exception handling, or automated trade promotion deductions).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. Architect and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails, ensuring seamless integration with complex enterprise ERPs (e.g., SAP), Transportation Management Systems (TMS), Warehouse Management Systems (WMS), and massive IoT/telematics data streams.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for CPG and Mobility Supply Chains, scaling knowledge across the organization regarding the nuances of fast-moving consumer goods and asset-heavy fleet operations.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within highly complex Supply Chain, CPG/Beverage, or Fleet Logistics environments. This includes defining AI roadmaps, building compelling ROI/TCO business cases for large-scale distribution networks, and guiding technical implementations through to value realization.

• Deep understanding of supply chain business processes native to high-volume manufacturing and mobility (such as Direct Store Delivery (DSD), Order-to-Cash, Procure-to-Pay, Fleet Lifecycle Management, or Sales & Operations Planning) with the ability to translate high-level operational needs into specific AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent dispatch assistants, or automated extraction of data from complex vendor contracts and logistics documentation).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies for handling massive, high-velocity transactional and IoT datasets.

• Strong presentation skills to both internal and external stakeholders (including supply chain executives, logistics directors, and enterprise IT leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, supply chain management, logistics, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations suited for enterprise-grade supply chain environments.

• Familiarity with CPG and fleet-specific systems (e.g., SAP APO/IBP, Blue Yonder, Manhattan, or major telematics/fleet tracking platforms).

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Your Impact on our Mission

Zocdoc’s most important asset is our people. As a Staff Data Scientist, Marketplace, you’ll play a meaningful role in transforming healthcare by shaping how millions of patients discover and book care.

The Marketplace team is responsible for driving patient demand, optimizing the search and booking experience, and ensuring a healthy, high-performing two-sided marketplace. This includes areas such as Growth, Search, Insurance, and Patient Home.

As a strategic technical leader on the Marketplace Analytics team, you’ll design and scale analytical frameworks that inform product and growth strategy, guide experimentation, and optimize performance across the patient funnel—from acquisition through booking and retention. Your work will directly improve marketplace liquidity, efficiency, and long-term value.

You’ll enjoy this role if you are…

• Passionate about transforming healthcare through data and turning complex datasets into meaningful product insights

• A force multiplier who loves mentoring and enabling teams to deliver scalable, high-impact work

• Comfortable operating at the intersection of business strategy, data science, and product innovation

• Energized by complex, ambiguous problems and skilled at translating them into structured analytical solutions

• Personally motivated by building systems, frameworks, and capabilities that outlast any one analysis

• Serious about your work, but not about yourself — you value rigor and humility equally

Your day to day is…

• Partnering with Product, Marketing, Finance, Engineering, and Commercial teams to translate insights into actionable product and business strategy

• Architecting and executing experimentation frameworks, including A/B testing design, power analysis, variance reduction, and advanced causal inference methodologies (e.g., DiD, synthetic controls, propensity modeling, causal forests)

• Identifying, designing, and guiding the development of analytical models (e.g., LTV, survival, uplift models) to improve provider utilization, acquisition, retention, patient engagement, and client ROI

• Establishing scalable statistical frameworks and best practices to quantify the impact of product and commercial initiatives

• Partnering with Engineering to ensure data quality, event instrumentation, feature availability, and scalable pipelines that enable trustworthy analysis

• Mentoring and upskilling analysts and data scientists, raising the bar for analytical rigor and impact across Zocdoc

• Advancing AI-assisted analytical workflows to streamline recurring analysis and decision support

• Working with cutting edge GenAI tools and technology

You’ll be successful in this role if you have…

• An unrelenting desire to build more equitable, inclusive, and diverse workplaces, and view this role as an opportunity to create positive change

• 8+ years of experience in data science, machine learning, or related quantitative roles

• Proven success developing LTV models, survival models, uplift models, or similar predictive frameworks for customer targeting and optimization

• Deep expertise in SQL and Python (pandas, NumPy, scikit-learn, statsmodels)

• Strong knowledge of experimentation and causal inference, including statistical power analysis, experiment design, and quasi-experimental methodologies

• Experience shaping and scaling analytics functions—not just building individual models

• Experience working in cross-functional product organizations

• The ability to integrate generative AI tools into daily workflows to automate tasks, foster innovation, and maximize productivity

• Superb communication skills and the ability to influence non-technical stakeholders

• The mentality of an entrepreneur/owner with a strong bias to action

• Humility — you treat all people with dignity and respect, regardless of title or tenure

Nice to have

• Master’s or PhD in Statistics, Economics, Applied Math, or another quantitative field

• Experience within a SaaS organization

Benefits

• Flexible, hybrid work environment at our convenient Soho location

• Unlimited Vacation

• 100% paid employee health benefit options (including medical, dental, and vision)

• Commuter Benefits

• 401(k) with employer funded match

• Corporate wellness program with Wellhub

• Sabbatical leave (for employees with 5+ years of service)

• Competitive paid parental leave and fertility/family planning reimbursement

• Cell phone reimbursement

• Catered lunch everyday along with beverages and snacks

• Employee Resource Groups and ZocClubs to promote shared community and belonging

• Great Place to Work Certified

Role Description

• As an Applied AI Engineer you are pushing the envelope in solving business-critical problems for our customers. You will be working with our most strategic customers, understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Engineers specialize in domains (e.g. supply chain) and industries

Requirements:

• 7+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

About the Role

The New York Times is looking for a Senior Software Engineer, with a focus on ML platform development, to join our Machine Learning & AI team to help solve creative challenges around Machine Learning infrastructure.

Machine Learning (ML) at the New York Times enhances the experience of our 150 million digital readers from around the globe and grows our subscriber base through content recommendations and personalizations.

The Machine Learning & AI team builds and maintains the infrastructure that hosts all of The New York Times real-time ML inference models, including both data and compute. Our partners are Data Scientists that build and deploy their ML models on the ML platform. On the other end, our partners are engineering systems that call these hosted models at scale with low-latency and Service Level Agreements (SLAs) guaranteed by our platform.

This role is a hybrid role, based in New York City. You will report to the Engineering Manager of the Machine Learning Platform team.

Responsibilities:

• You will research, develop, and deploy infrastructure for the Machine Learning Platform that supports large-scale multi-tenant workloads

• You will build a platform to train and test algorithms that provide real-time content recommendations and personalization to our readers

• You will enhance ML platform's CI/CD and integration testing capabilities

• You will build a platform that supports the full Machine Learning workflow; from model training, testing and production deployment at high scale and low latency

• Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world

Basic Qualifications:

• 5+ years of direct relevant experience in MLOps or DevOps, including experience operating large systems in a production environment

• Experience deploying and monitoring systems using cloud infrastructure (GCP or AWS)

• Experience working with Kubernetes, Docker, and CI/CD (Drone, Argo, Jenkins, etc)

• Experience leading the development of large-scale, data-driven, distributed multi-tenant systems

• Proficiency with at least one high-level programming language like Python or Go

Preferred Qualifications:

• Experience with any of the these technologies: Terraform, Airflow, SQL/BigQuery, BigTable or other NoSQL datastores such as Cassandra, DynamoDB, Redis

• Familiarity with ML tooling such as Triton, TensorFlow, scikit-learn

• Experience building the infrastructure that power real-world machine learning applications like recommendation systems, bandits, etc.

• Experience engaging with partners to understand pain points, observe patterns, and identify opportunities for improvements

This role requires limited on-call hours. An on-call schedule will be determined when you join, taking into account team size and other variables.

REQ-019502

#LI-hybrid

About the Role

Machine Learning (ML) at the New York Times enhances the experience of our 150 million digital readers from around the globe and grows our subscriber base through content recommendations and personalizations.

The Machine Learning Platform (MLP) team builds and maintains the infrastructure that hosts all of The New York Times real-time ML inference models, including both data and compute. Our partners are Data Scientists that build and deploy their ML models on the ML platform. On the other end, our partners are engineering systems that call these hosted models with low-latency and Service Level Agreements (SLAs) guaranteed by the MLP.

We are looking for a Software Engineer, with a focus on MLOps, to join our Machine Learning Platform team to help solve creative challenges around Machine Learning infrastructure for the New York Times.

This role is a hybrid role, based in New York City. You will report to the Engineering Manager of the Machine Learning Platform team.

Responsibilities:

• You will research, develop, and deploy infrastructure for the Machine Learning Platform at scale

• You will build infrastructure to train and test algorithms that provide real-time content recommendations and personalization

• You will enhance ML platform's CI/CD and integration testing capabilities

• You will promote developer productivity by rolling out capabilities that maximize self-sufficiency and minimize the toil in learning those capabilities in building scalable applications with high speed, quality, and performance

• Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world

Basic Qualifications:

• 2+ years of direct relevant experience in MLOps or DevOps, including experience operating large systems in a production environment

• Experience deploying and monitoring systems using cloud infrastructure (GCP or AWS)

• Experience working with Kubernetes, Docker, and CI/CD

• Familiarity with at least one high-level programming language like Python or Go

Preferred Qualifications:

• Experience with any of the these technologies: Terraform, Airflow, SQL/BigQuery, Drone, Triton, TensorFlow, scikit-learn, DataDog

• Experience building real-world machine learning applications like recommendation systems, bandits, etc.

• Experience engaging with stakeholders to understand pain points, observe patterns, and identify opportunities for improvements

This role may require limited on-call hours. An on-call schedule will be determined when you join, taking into account team size and other variables.

REQ-019501

#LI-Hybrid

About the Role

Charlie Health leads the nation in high-acuity virtual behavioral care, having delivered life-saving treatment to more than 100,000 clients nationwide. Our Matching and Outcomes team builds products to measure and improve clinical outcomes, including provider recommendation systems for clients and clinical decision support tools, enabling durable and effective treatment. You'll create predictive models and workflows to match patients with the best-fit therapists and peers, and enable clinicians to tailor care to each client's individual needs. The systems you build will help clinicians condense and synthesize knowledge across our clients and providers into actionable guidance at every moment. If you care about using data-driven insights to improve outcomes, this team is for you.

Responsibilities

• Design, train, and evaluate machine learning models and AI systems that drive meaningful business impact
• Identify opportunities to introduce or improve data-driven algorithms and automated solutions in partnership with clinical, design, data science, and product teams
• Pioneer new approaches to measure and validate outcomes impact through advanced experimentation and causal inference techniques
• Utilize ML infrastructure to serve model inferences in real time using live data streams
• Write production code within a backend Python monolith to invoke ML predictions.
• Foster a culture of collaboration and learning across engineering, product, and design through mentoring, documentation, presentations, and knowledge sharing

Requirements

• A bachelor’s degree in a technical field, plus 4+ years of professional experience developing and delivering production software, including machine learning models; OR
• A master’s or doctorate degree in a technical field, plus 2+ years of professional experience developing and delivering production software, including machine learning models.
• Expertise in Python for ML (pandas, sklearn, etc.) and backend server development (FastAPI)
• Strong SQL skills and experience navigating analytical and transactional databases to assess and curate high-quality datasets
• Experience developing agentic AI workflows is a plus
• Excellent at managing ambiguity—able to break down big, messy problems into smaller parts with tractable solutions and clear iterations
• Growth mindset and sense of humor; you welcome feedback, adapt quickly in a fast-paced environment, and foster a culture of learning and fun
• This role requires 4 days per week in our NYC office (Flatiron District). The team is entirely based out of NYC.

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

The total target base compensation for this role will be between $170,000-$220,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits.

About the Role

Charlie Health leads the nation in high-acuity virtual behavioral care, having delivered life-saving treatment to more than 100,000 clients nationwide. Our Matching and Outcomes team builds products to measure and improve clinical outcomes, including provider recommendation systems for clients and clinical decision support tools, enabling durable and effective treatment. You'll create predictive models and workflows to match patients with the best-fit therapists and peers, and enable clinicians to tailor care to each client's individual needs. The systems you build will help clinicians condense and synthesize knowledge across our clients and providers into actionable guidance at every moment. If you care about using data-driven insights to improve outcomes, this team is for you.

Responsibilities

• Design, train, and evaluate machine learning models and AI systems that drive meaningful business impact
• Identify opportunities to introduce or improve data-driven algorithms and automated solutions in partnership with clinical, design, data science, and product teams
• Pioneer new approaches to measure and validate outcomes impact through advanced experimentation and causal inference techniques
• Utilize ML infrastructure to serve model inferences in real time using live data streams
• Write production code within a backend Python monolith to invoke ML predictions.
• Foster a culture of collaboration and learning across engineering, product, and design through mentoring, documentation, presentations, and knowledge sharing

Requirements

• A bachelor’s degree in a technical field, plus 4+ years of professional experience developing and delivering production software, including machine learning models; OR
• A master’s or doctorate degree in a technical field, plus 2+ years of professional experience developing and delivering production software, including machine learning models.
• Expertise in Python for ML (pandas, sklearn, etc.) and backend server development (FastAPI)
• Strong SQL skills and experience navigating analytical and transactional databases to assess and curate high-quality datasets
• Experience developing agentic AI workflows is a plus
• Excellent at managing ambiguity—able to break down big, messy problems into smaller parts with tractable solutions and clear iterations
• Growth mindset and sense of humor; you welcome feedback, adapt quickly in a fast-paced environment, and foster a culture of learning and fun
• This role requires 4 days per week in our NYC office (Flatiron District). The team is entirely based out of NYC.

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

The total target base compensation for this role will be between $170,000-$220,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits.

What You’ll Do

We’re seeking a talented and experienced Senior Engineer for Network Observability to join our Network Observability team. In this role, you will be a key player in designing, developing, and maintaining the monitoring, telemetry, and observability systems that keep CoreWeave’s GPU cloud network operating reliably and at scale. You’ll focus on building solutions that provide real-time insights into network performance, ensuring that issues are detected proactively and resolved quickly.

Your mission? To empower CoreWeave’s network with advanced observability: robust metrics, powerful analytics, and automated alerting—so well-tuned that any anomalies become clear before they ever impact our customers.

• Develop, optimize, and maintain network observability platforms. Use your skills in Python and Golang to create and automate collectors, exporters, and dashboards that provide deep visibility into network health and performance.
• Collaborate with Network Engineering and Platform teams to ingest and unify logs, metrics, and events from a variety of platforms (Arista EOS, NVIDIA Cumulus Linux, Nokia SR OS, SR Linux, etc.) into a single observability pipeline.
• Design and implement scalable telemetry solutions using protocols like gNMI, SNMP, and streaming analytics. Ensure advanced alerting and anomaly detection with tools such as Prometheus, Grafana, and Alertmanager.
• Work closely with network developers, site reliability engineers, and security teams to integrate observability solutions across the broader infrastructure. Participate in design discussions, RFCs, and architectural decisions.
• Join a rotating on-call schedule to troubleshoot and resolve observability-related issues. Provide timely support to operations teams, quickly isolating and fixing problems when they arise.
• Guide junior team members, share best practices, and foster a culture of continuous learning and improvement within the observability domain.

Investing in our people is one of our top priorities, and we value candidates who can bring their diversified experiences to our teams. Here are some qualities we’ve found compatible with our team. We'd love to talk about whether this aligns with your experience and interests and what you’re excited to work on next.

Who You Are

Minimum Qualifications

• Deep familiarity with Prometheus, Grafana, Alertmanager, gNMI, and SNMP. Experience writing or extending custom metric collectors/exporters is a plus.
• Experience as a Network Engineer, SRE, Software Developer, or Systems Administrator in large-scale environments. A track record of building and operating robust telemetry and monitoring solutions is a plus.
• Passion for automating tasks and processes. You find satisfaction in creating workflows that handle repetitive tasks and reduce human error to near zero.
• Comfortable containerizing solutions in Kubernetes, designing, building, and deploying container-based workloads efficiently.
• Proficient with Python, Go, and Bash, plus familiarity with configuration management and templating tools (e.g., Ansible, Jinja2). .
• Strong knowledge of Linux systems and IP networking concepts, with hands-on experience in routing, switching, and network troubleshooting.
• Practical knowledge with a variety of platforms, including Arista EOS, NVIDIA Cumulus Linux, Nokia SR OS, and SR Linux.
• Collaborative, humble, and always ready to help others while staying open to learning from more senior colleagues.

Preferred Qualifications

• College Education: Bachelor’s degree in Computer Science or a related field.
• Machine Learning for Anomaly Detection: Hands-on experience applying ML techniques or tools (e.g., TensorFlow, scikit-learn) to proactively detect performance or security anomalies in network traffic.
• Network Certifications: Certifications like CCNA, CCNP, or similar.
• Advanced Metrics & Analytics: Hands-on experience with data pipelines, event correlation, or anomaly detection in large-scale environments.
• Distributed Tracing: Familiarity with OpenTelemetry, Jaeger, or Zipkin for end-to-end tracing across microservices and network components.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!  

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

What You’ll Do

We’re seeking a talented and experienced Senior Engineer for Network Observability to join our Network Observability team. In this role, you will be a key player in designing, developing, and maintaining the monitoring, telemetry, and observability systems that keep CoreWeave’s GPU cloud network operating reliably and at scale. You’ll focus on building solutions that provide real-time insights into network performance, ensuring that issues are detected proactively and resolved quickly.

Your mission? To empower CoreWeave’s network with advanced observability: robust metrics, powerful analytics, and automated alerting—so well-tuned that any anomalies become clear before they ever impact our customers.

• Develop, optimize, and maintain network observability platforms. Use your skills in Python and Golang to create and automate collectors, exporters, and dashboards that provide deep visibility into network health and performance.
• Collaborate with Network Engineering and Platform teams to ingest and unify logs, metrics, and events from a variety of platforms (Arista EOS, NVIDIA Cumulus Linux, Nokia SR OS, SR Linux, etc.) into a single observability pipeline.
• Design and implement scalable telemetry solutions using protocols like gNMI, SNMP, and streaming analytics. Ensure advanced alerting and anomaly detection with tools such as Prometheus, Grafana, and Alertmanager.
• Work closely with network developers, site reliability engineers, and security teams to integrate observability solutions across the broader infrastructure. Participate in design discussions, RFCs, and architectural decisions.
• Join a rotating on-call schedule to troubleshoot and resolve observability-related issues. Provide timely support to operations teams, quickly isolating and fixing problems when they arise.
• Guide junior team members, share best practices, and foster a culture of continuous learning and improvement within the observability domain.

Investing in our people is one of our top priorities, and we value candidates who can bring their diversified experiences to our teams. Here are some qualities we’ve found compatible with our team. We'd love to talk about whether this aligns with your experience and interests and what you’re excited to work on next.

Who You Are

Minimum Qualifications

• Deep familiarity with Prometheus, Grafana, Alertmanager, gNMI, and SNMP. Experience writing or extending custom metric collectors/exporters is a plus.
• Experience as a Network Engineer, SRE, Software Developer, or Systems Administrator in large-scale environments. A track record of building and operating robust telemetry and monitoring solutions is a plus.
• Passion for automating tasks and processes. You find satisfaction in creating workflows that handle repetitive tasks and reduce human error to near zero.
• Comfortable containerizing solutions in Kubernetes, designing, building, and deploying container-based workloads efficiently.
• Proficient with Python, Go, and Bash, plus familiarity with configuration management and templating tools (e.g., Ansible, Jinja2). .
• Strong knowledge of Linux systems and IP networking concepts, with hands-on experience in routing, switching, and network troubleshooting.
• Practical knowledge with a variety of platforms, including Arista EOS, NVIDIA Cumulus Linux, Nokia SR OS, and SR Linux.
• Collaborative, humble, and always ready to help others while staying open to learning from more senior colleagues.

Preferred Qualifications

• College Education: Bachelor’s degree in Computer Science or a related field.
• Machine Learning for Anomaly Detection: Hands-on experience applying ML techniques or tools (e.g., TensorFlow, scikit-learn) to proactively detect performance or security anomalies in network traffic.
• Network Certifications: Certifications like CCNA, CCNP, or similar.
• Advanced Metrics & Analytics: Hands-on experience with data pipelines, event correlation, or anomaly detection in large-scale environments.
• Distributed Tracing: Familiarity with OpenTelemetry, Jaeger, or Zipkin for end-to-end tracing across microservices and network components.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!  

To fulfil our obligation to protect client data, successful applicants offered employment with CoreWeave will be required to complete a basic criminal record check, conducted in compliance with GDPR. Employment offers are conditional upon receiving satisfactory check results

What You’ll Do
The Field Engineering team at Weights & Biases plays a vital role in ensuring customer success and adoption of our platform. As part of this team, we partner with Sales, Support, Product, and Engineering to lead technical success after the sales process. We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs).

About the Role

We’re hiring a Post-Sales AI Solutions Engineer (AISE), Scale Customer Success to help customers successfully implement and scale AI/ML workflows and GenAI/agentic applications on Weights & Biases. You will design and deliver technical enablement and adoption programs that reach many customers at once, create reusable assets that improve self-serve success, and use product signals and feedback loops to continuously improve outcomes.

Key Responsibilities 

1. Run 1-to-many onboarding and enablement programs
   Own and deliver scalable onboarding and adoption motions (webinars, cohort sessions, group training, and office hours) that help customers get to value quickly and consistently.
2. Build reusable technical assets that drive self-serve success
   Create and maintain playbooks, reference architectures, templates, sample code/notebooks, and troubleshooting guides that standardize best practices and reduce repeated 1:1 work.
3. Operate the scaled motion using signals and feedback loops
   Use product usage signals and customer patterns to segment and trigger the right interventions, track program impact (activation, time-to-first-value, feature adoption), and feed recurring insights back to Support, Product, and Field Engineering to continuously improve the scaled journey.

Who You Are

• 3–5 years of relevant experience in a similar role
• Strong programming proficiency in Python
• Experience with deep learning frameworks (TensorFlow/Keras, PyTorch Lightning) and tools (e.g., Streamlit, LangChain)
• Familiarity with cloud platforms (AWS, GCP, Azure)
• Excellent communication and presentation skills, both written and verbal
• Organized and outcomes-driven: you can run programs, measure impact, and iterate.

Preferred

• Experience with GenAI and LLMs
• Proficiency with HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, or Ray
• Experience with hyperparameter optimization solutions
• Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes
• Familiarity with data pipeline tools

Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love collaborating with top AI teams to solve real-world ML and GenAI challenges
• You’re curious about how to optimize AI workflows at scale
• You’re an expert in Python and ML tooling, with a passion for enabling customer success

Why Us?
We work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

ML & Data Science is at the heart of Lyft's products and decision-making. ML and Data Science professionals at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Lyft Ads is Lyft's advertising platform, connecting brands with high-intent audiences across the rideshare journey. Our offerings span in-app ad formats, in-car tablet experiences, bikeshare and station sponsorships, and programmatic integrations—enabling advertisers to reach riders at key moments of engagement. These products power high-impact use cases across brand awareness, performance marketing, audience targeting, and measurement for enterprise advertisers.

We are seeking an Algorithms Science Manager to lead a team of Data Scientists, Applied Scientists, and Machine Learning Engineers building the algorithmic backbone of Lyft Media. In this role, you will shape the vision, define the roadmap, and drive execution for projects that improve ad relevance, optimize yield, enhance targeting and measurement, and deliver measurable value to our advertising partners. You'll collaborate closely with Product, Engineering, Design, and Sales teams to build models, experimentation frameworks, and production ML systems that inform strategy and power product innovation.

This is a high-visibility, high-impact role with direct influence on Lyft's advertising platform and revenue growth. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, and experimentation; strong business acumen in ads or marketplace contexts; and a proven track record of leading multi-disciplinary technical teams in fast-paced, cross-functional environments.

Responsibilities:

• Lead, mentor, and grow a high-performing, multi-disciplinary team spanning Applied Science. Data Science, and Machine Learning Engineering for Lyft Media.
• Define and execute the technical vision and roadmap for the team, ensuring alignment with overall business strategy and revenue goals across research, modeling, and production ML.
• Design, develop, and deploy algorithms and ML systems that power core advertising capabilities—including ad targeting, audience segmentation, bid optimization, attribution, and yield management.
• Partner with Product, Engineering, and Design to integrate solutions into scalable, production-grade ad serving and measurement systems.
• Establish robust experimentation and causal inference frameworks to measure the impact of algorithmic changes on advertiser outcomes, rider experience, and platform revenue.
• Bridge the gap between research and production—ensuring that applied science innovations translate into reliable, maintainable ML systems at scale.
• Conduct deep analyses of complex, large-scale datasets to uncover opportunities for revenue growth, advertiser performance improvement, and enhanced user experience.
• Champion data-driven decision-making, ensuring that product and go-to-market decisions are informed by rigorous quantitative analysis.
• Drive innovation by staying current with emerging research, technologies, and industry best practices in computational advertising, optimization, and applied machine learning.

Experience:

• PhD (preferred) or Master's degree in a quantitative field such as Machine Learning, Computer Science, Statistics, Engineering, or a related discipline; or equivalent practical experience.
• 8+ years of progressive experience in machine learning, optimization, or causal inference, including building and deploying algorithms in production systems.
• 3+ years of people management experience leading multi-disciplinary technical teams (data science, applied science, and/or ML engineering), with a proven ability to mentor, develop, and retain top talent.
• Demonstrated ability to set a strategic vision for a technical team and translate it into impactful, scalable solutions that drive measurable business outcomes.
• Deep expertise in machine learning, experimental design, causal inference, and statistical methodologies, with a track record of applying them to high-stakes product or marketplace decisions.
• Strong understanding of ML engineering best practices—model training infrastructure, feature pipelines, model serving, and monitoring in production environments.
• Experience in advertising technology, media measurement, or marketplace optimization is strongly preferred.
• Experience navigating complex, ambiguous problem spaces and guiding teams through prioritization, tradeoffs, and execution.
• Strong communication and influence skills, with the ability to engage both technical and executive stakeholders, align priorities, and build consensus.
• Hands-on proficiency with large-scale data processing tools and machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the New York City area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. 

Lyft Ads is one of Lyft’s newest and fastest-growing businesses, focused on building the world’s largest transportation media network. Our mission is to help brands reach riders during key moments of their journey—before, during, and after a ride—by delivering meaningful, contextually relevant ad experiences. We operate at the intersection of mobility data, real-time decision systems, and AI-powered personalization, enabling advertisers to run high-impact campaigns with measurable outcomes.

We are seeking an Algorithms Scientist  to help build the next generation of ads relevance, targeting, optimization, and measurement algorithms that power the Lyft Ads platform. In this role, you will work across large-scale datasets and complex real-time systems to design, prototype, and deploy production-grade machine learning models. You’ll collaborate closely with Engineering, Product, Data Science, and Sales to translate ambiguous business and advertiser needs into rigorous algorithmic solutions that improve ad performance, enhance marketplace efficiency, and drive meaningful revenue growth.

This is a high-impact, highly technical role within a rapidly scaling business line. The ideal candidate brings strong applied machine learning intuition, hands-on modeling experience, and the ability to write clean, efficient production code. You will play a critical role in shaping how advertisers connect with Lyft riders—pushing the boundaries of personalization, measurement, and real-time optimization in a dynamic marketplace.

Responsibilities:

• Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.
• Own the end-to-end lifecycle of modeling projects — including problem definition, data exploration, feature engineering, model development, offline evaluation, deployment, and monitoring.
• Collaborate closely with Ads Engineering to integrate models into real-time ad-serving and batch decision systems, ensuring performance across latency, scalability, and reliability constraints.
• Analyze large-scale mobility, behavioral, and ads performance datasets to identify patterns, surface opportunities, and guide ML and AI driven product improvements.
• Implement rigorous model evaluation frameworks, including offline metrics, statistical tests, calibration, sensitivity analysis, and A/B experimentation to validate both model impact and system-level outcomes.
• Build robust training pipelines, feature transformations, and scoring infrastructure, ensuring reproducibility, observability, and long-term maintainability.
• Partner with Product, Engineering, and Sales to translate ambiguous advertiser goals (e.g., increased conversions, reach efficiency, brand lift) into measurable requirements and success metrics.
• Investigate and resolve model behavior issues, production regressions, calibration drift, and performance anomalies in close partnership with Ads Infra teams.
• Drive innovation by staying current with advances in ML for ranking, recommendation, causal inference, optimization, and ads measurement — and proactively identifying opportunities to apply them.
• Contribute to Lyft Ads’ modeling and experimentation infrastructure, through model cards, documentation, reproducibility standards, and code quality improvements.

Experience:

• Master’s, or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative fields; or equivalent applied industry experience.
• 3–5 years of hands-on ML/applied science experience, ideally involving production models, large-scale systems, or ads/recommendation/relevance domains.
  Strong proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn; ability to write clean, efficient, production-adjacent code.
• Experience working with large-scale datasets and distributed data tools (Spark, Snowflake, Presto, Databricks).
• Practical experience building and evaluating:
• Ranking and relevance models
• Optimization or pacing algorithms
• Predictive models for CTR, CVR, or user response
• Causal or experimentation-based measurement methods
• Understanding of online/offline evaluation techniques, including:
• Offline metrics (AUC, NDCG, MRR, calibration)
• A/B testing methodologies
• Bias correction and counterfactual estimation
• Ability to solve ambiguous problems by structuring analyses, evaluating trade-offs, and proposing algorithmic solutions grounded in scientific rigor.
• Strong communication skills, with an ability to clearly explain model behavior, constraints, trade-offs, and recommendations to engineering, product, and sales partners.
• Demonstrated ownership of modeling work, including debugging, monitoring, documentation, and iteration after deployment.
• Curiosity, initiative, and a track record of delivering measurable improvements through high-quality modeling.

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $128,000 - $160,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process. 

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. 

Lyft Ads is one of Lyft’s newest and fastest-growing businesses, focused on building the world’s largest transportation media network. Our mission is to help brands reach riders during key moments of their journey—before, during, and after a ride—by delivering meaningful, contextually relevant ad experiences. We operate at the intersection of mobility data, real-time decision systems, and AI-powered personalization, enabling advertisers to run high-impact campaigns with measurable outcomes.

We are seeking an Algorithms Scientist  to help build the next generation of ads relevance, targeting, optimization, and measurement algorithms that power the Lyft Ads platform. In this role, you will work across large-scale datasets and complex real-time systems to design, prototype, and deploy production-grade machine learning models. You’ll collaborate closely with Engineering, Product, Data Science, and Sales to translate ambiguous business and advertiser needs into rigorous algorithmic solutions that improve ad performance, enhance marketplace efficiency, and drive meaningful revenue growth.

This is a high-impact, highly technical role within a rapidly scaling business line. The ideal candidate brings strong applied machine learning intuition, hands-on modeling experience, and the ability to write clean, efficient production code. You will play a critical role in shaping how advertisers connect with Lyft riders—pushing the boundaries of personalization, measurement, and real-time optimization in a dynamic marketplace.

Responsibilities:

• Design, develop, and deploy production-grade machine learning models and algorithms that power core Lyft Ads capabilities, such as ad relevance, targeting, ranking, bid optimization, pacing, campaign delivery, and measurement.
• Own the end-to-end lifecycle of modeling projects — including problem definition, data exploration, feature engineering, model development, offline evaluation, deployment, and monitoring.
• Collaborate closely with Ads Engineering to integrate models into real-time ad-serving and batch decision systems, ensuring performance across latency, scalability, and reliability constraints.
• Analyze large-scale mobility, behavioral, and ads performance datasets to identify patterns, surface opportunities, and guide ML and AI driven product improvements.
• Implement rigorous model evaluation frameworks, including offline metrics, statistical tests, calibration, sensitivity analysis, and A/B experimentation to validate both model impact and system-level outcomes.
• Build robust training pipelines, feature transformations, and scoring infrastructure, ensuring reproducibility, observability, and long-term maintainability.
• Partner with Product, Engineering, and Sales to translate ambiguous advertiser goals (e.g., increased conversions, reach efficiency, brand lift) into measurable requirements and success metrics.
• Investigate and resolve model behavior issues, production regressions, calibration drift, and performance anomalies in close partnership with Ads Infra teams.
• Drive innovation by staying current with advances in ML for ranking, recommendation, causal inference, optimization, and ads measurement — and proactively identifying opportunities to apply them.
• Contribute to Lyft Ads’ modeling and experimentation infrastructure, through model cards, documentation, reproducibility standards, and code quality improvements.

Experience:

• Master’s, or PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, Engineering, or related quantitative fields; or equivalent applied industry experience.
• 3–5 years of hands-on ML/applied science experience, ideally involving production models, large-scale systems, or ads/recommendation/relevance domains.
  Strong proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn; ability to write clean, efficient, production-adjacent code.
• Experience working with large-scale datasets and distributed data tools (Spark, Snowflake, Presto, Databricks).
• Practical experience building and evaluating:
• Ranking and relevance models
• Optimization or pacing algorithms
• Predictive models for CTR, CVR, or user response
• Causal or experimentation-based measurement methods
• Understanding of online/offline evaluation techniques, including:
• Offline metrics (AUC, NDCG, MRR, calibration)
• A/B testing methodologies
• Bias correction and counterfactual estimation
• Ability to solve ambiguous problems by structuring analyses, evaluating trade-offs, and proposing algorithmic solutions grounded in scientific rigor.
• Strong communication skills, with an ability to clearly explain model behavior, constraints, trade-offs, and recommendations to engineering, product, and sales partners.
• Demonstrated ownership of modeling work, including debugging, monitoring, documentation, and iteration after deployment.
• Curiosity, initiative, and a track record of delivering measurable improvements through high-quality modeling.

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the New York City area is $128,000 - $160,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process. 

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

ML & Data Science is at the heart of Lyft's products and decision-making. ML and Data Science professionals at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Lyft Ads is Lyft's advertising platform, connecting brands with high-intent audiences across the rideshare journey. Our offerings span in-app ad formats, in-car tablet experiences, bikeshare and station sponsorships, and programmatic integrations—enabling advertisers to reach riders at key moments of engagement. These products power high-impact use cases across brand awareness, performance marketing, audience targeting, and measurement for enterprise advertisers.

We are seeking an Algorithms Science Manager to lead a team of Data Scientists, Applied Scientists, and Machine Learning Engineers building the algorithmic backbone of Lyft Media. In this role, you will shape the vision, define the roadmap, and drive execution for projects that improve ad relevance, optimize yield, enhance targeting and measurement, and deliver measurable value to our advertising partners. You'll collaborate closely with Product, Engineering, Design, and Sales teams to build models, experimentation frameworks, and production ML systems that inform strategy and power product innovation.

This is a high-visibility, high-impact role with direct influence on Lyft's advertising platform and revenue growth. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, and experimentation; strong business acumen in ads or marketplace contexts; and a proven track record of leading multi-disciplinary technical teams in fast-paced, cross-functional environments.

Responsibilities:

• Lead, mentor, and grow a high-performing, multi-disciplinary team spanning Applied Science. Data Science, and Machine Learning Engineering for Lyft Media.
• Define and execute the technical vision and roadmap for the team, ensuring alignment with overall business strategy and revenue goals across research, modeling, and production ML.
• Design, develop, and deploy algorithms and ML systems that power core advertising capabilities—including ad targeting, audience segmentation, bid optimization, attribution, and yield management.
• Partner with Product, Engineering, and Design to integrate solutions into scalable, production-grade ad serving and measurement systems.
• Establish robust experimentation and causal inference frameworks to measure the impact of algorithmic changes on advertiser outcomes, rider experience, and platform revenue.
• Bridge the gap between research and production—ensuring that applied science innovations translate into reliable, maintainable ML systems at scale.
• Conduct deep analyses of complex, large-scale datasets to uncover opportunities for revenue growth, advertiser performance improvement, and enhanced user experience.
• Champion data-driven decision-making, ensuring that product and go-to-market decisions are informed by rigorous quantitative analysis.
• Drive innovation by staying current with emerging research, technologies, and industry best practices in computational advertising, optimization, and applied machine learning.

Experience:

• PhD (preferred) or Master's degree in a quantitative field such as Machine Learning, Computer Science, Statistics, Engineering, or a related discipline; or equivalent practical experience.
• 8+ years of progressive experience in machine learning, optimization, or causal inference, including building and deploying algorithms in production systems.
• 3+ years of people management experience leading multi-disciplinary technical teams (data science, applied science, and/or ML engineering), with a proven ability to mentor, develop, and retain top talent.
• Demonstrated ability to set a strategic vision for a technical team and translate it into impactful, scalable solutions that drive measurable business outcomes.
• Deep expertise in machine learning, experimental design, causal inference, and statistical methodologies, with a track record of applying them to high-stakes product or marketplace decisions.
• Strong understanding of ML engineering best practices—model training infrastructure, feature pipelines, model serving, and monitoring in production environments.
• Experience in advertising technology, media measurement, or marketplace optimization is strongly preferred.
• Experience navigating complex, ambiguous problem spaces and guiding teams through prioritization, tradeoffs, and execution.
• Strong communication and influence skills, with the ability to engage both technical and executive stakeholders, align priorities, and build consensus.
• Hands-on proficiency with large-scale data processing tools and machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn).

Benefits:

• Great medical, dental, and vision insurance options with additional programs available when enrolled
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• 401(k) plan with company match to help save for your future
• In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Subsidized commuter benefits
• Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Senior Manager, Data Science

Portfolio Management and Loss Mitigation

The Role: 

SoFi is seeking an inspirational and deeply experienced Senior Manager to lead the Portfolio Management and Loss Mitigation function in our Risk Data Science team. Reporting to the Head of Risk Data Science, this role will lead the development, deployment, and governance of portfolio management and loss mitigation models for all credit products.

The ideal candidate is a hands-on leader who can transition the team from traditional modeling to next-generation machine learning platforms, leveraging emerging data sources (e.g., cash flow, alternative bureaus) to significantly improve portfolio performance, reduce losses, and ensure rigorous adherence to Model Risk Management (MRM) standards. This role requires exceptional organizational leadership, an ability to influence stakeholders, and proven success in delivering complex models into a regulated production environment.

What You'll Do: 

• Portfolio Management and Loss Mitigation Excellence: Directly oversee the development and deployment of Next Generation models designed to manage and mitigate portfolio losses while maintaining loss guardrails.
• Drive Next-Generation Capabilities: Incorporate industry trends and advanced techniques (NLP, Graph Mining, LLMs, Deep Learning) to solve complex, high-impact risk problems where established principles may not fully apply.
• Alternative Data Strategy: Spearhead the evaluation and integration of alternative data sources (Macro-Economic variables, tri-bureau, LexisNexis, cash flow data) to enhance predictive power across all credit products.
• Lead the current team of high-performing Staff and Senior Data Scientists. Recruit, mentor, and foster talent through deliberate interactions, succession planning, and creating a high-accountability, low-ego culture.
• Model Risk Management (MRM): Act as the primary owner for all models in the portfolio, ensuring robust documentation, monitoring, and successfully navigating the 2nd Line of Defense (2LOD) review and approval process (SR 11-7 familiarity is mandatory).
• Stakeholder Alignment: Interact and negotiate with senior management and external stakeholders to reconcile competing views and drive critical, high-impact business decisions.
• Automation and Efficiency: Lead efforts to automate model monitoring and governance processes to create scalable and auditable infrastructure.

What You'll Need:

• Experience: 8+ years of progressive experience in credit risk, modeling, and data science within a regulated financial institution (FinTech, Bank, or similar), with at least 3 years in a people management role, managing technical staff.
• Education: Master’s or Ph.D. degree in a quantitative field (Statistics, Computer Science, Engineering, Operations Research, etc.).
• Technical Acumen: Deep expertise in advanced statistical and machine learning modeling techniques (e.g., Gradient Boosting, Deep Learning, Causal Inference).
• Regulatory Knowledge: Detailed working knowledge of model risk management standards (e.g., SR 11-7) and the ability to operate within a highly regulated environment.
• Tools & Platforms: Expert-level proficiency in Python (PySpark, scikit-learn, TensorFlow/PyTorch) and SQL/data warehouse technologies (e.g., Snowflake, Hive). Familiarity with modern MLOps platforms and cloud computing (AWS).
• 3+ years of progressive people management experience is required, including the ability to recruit, mentor, and foster talent within a team of high-performing Staff and Senior Data Scientists.
• Communication: Ability to distill highly complex analytical concepts into clear, concise, and compelling narratives for non-technical leadership.

Mid-level Applied AI Data Scientist (Tableau Focus)

Location: New York, NY (Hybrid – 3 days in-office) | Practice Area: Technology & Engineering | Type: Permanent

Turn data into insight, and insight into impact.

The Role
We are looking for a Data Scientist to join us, working at the intersection of data, machine learning, and business insight. In this role, you will apply statistical and machine learning techniques to real-world problems, helping clients unlock value from complex datasets. You will collaborate with cross-functional teams and contribute to the development of scalable, data-driven solutions.

What You’ll Do

• Apply statistical analysis, clustering, and probabilistic techniques to generate actionable business insights
• Work with structured and semi-structured datasets to identify patterns, trends, and anomalies
• Build and support NLP workflows, including named entity recognition (NER), entity resolution, and text classification
• Develop machine learning models with guidance from senior team members, contributing to evaluation and refinement
• Create Tableau dashboards and visualizations to communicate insights to technical and non-technical stakeholders

What We’re Looking For

• 3–7 years of experience in data science, analytics, or applied machine learning
• Strong foundation in statistics, probability, and core machine learning techniques (e.g., regression, classification, clustering)
• Hands-on experience with Python and libraries such as pandas, NumPy, and scikit-learn
• Experience building Tableau dashboards to clearly communicate data insights
• Ability to collaborate across teams and translate analytical findings into business-relevant outcomes

Bonus Points For

• Experience working with graph-like or network datasets
• Exposure to ML Ops practices such as experiment tracking, model versioning, and monitoring
• Hands-on experience with NLP techniques such as entity extraction or text processing
• Familiarity with cloud-based data or ML platforms
• Experience contributing to rapid prototyping or iterative AI solution development

Why Join Capco

• Deliver high-impact technology solutions for Tier 1 financial institutions
• Work in a collaborative, flat, and entrepreneurial consulting culture
• Access continuous learning, training, and industry certifications
• Be part of a team shaping the future of digital financial services
• Help shape the future of digital transformation across FS & Energy.

Use of Artificial Intelligence in Talent Acquisition
At Capco, we use artificial intelligence (AI) tools to support and enhance several parts of talent acquisition. This includes using AI-enabled features within LinkedIn to help source potential candidates, speeding up routine recruitment communications such as emails and creating compelling and brand-aligned job postings that accurately reflect role requirements, and AI-scheduling applications to improve the efficiency of interview coordination.

AI is used as a support tool only. All hiring decisions are made by talent acquisition and hiring teams.

We offer a competitive, people-first benefits package designed to support every aspect of your life:

Benefits

• Medical, Dental, Vision coverage
• 401(k) with employer match
• Maternity and paternity leave, plus dependent care support
• Tuition reimbursement and wellness reimbursement programs
• HSA, FSA, commuter benefits, and employee discount marketplace access

Inclusion at Capco
We’re committed to making our recruitment process accessible and straightforward for everyone. If you need any adjustments at any stage, just let us know – we’ll be happy to help. We value each person’s unique perspective and contribution. At Capco, we believe that being yourself is your greatest strength. Our #BeYourselfAtWork culture encourages individuality and collaboration – a mindset that shapes how we work with clients and each other every day.

We have been informed of several recruitment scams targeting the public. We strongly advise you to verify identities before engaging in recruitment related communication. All official Capco communication will be conducted via a Capco recruiter.

#LI-MB1

#LI-HYBRID

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

What you’ll do

About the internship

Stripe's Applied ML, Data Science, Risk, and Payments organizations are excited to offer PhD machine learning engineering internships for the summer of 2026. This is an exceptional opportunity to contribute to critical projects that directly enhance Stripe's suite of products, focusing on areas such as foundation models used for dozens of tasks e.g. fraud detection, enhanced support, and predicting user behavior.

As an intern, you'll tackle challenging problems at the intersection of finance, technology, and data. You'll have the chance to work on creative projects like the Stripe Assistant and the Stripe Foundation Model, which leverage machine learning to revolutionize how businesses interact with financial services and data.

Responsibilities

• Develop and deploy large-scale machine learning systems that drive significant business value across various domains.
• Engage in the end-to-end process of designing, training, improving, and launching machine learning models.
• Write production-scale ML models that will be deployed to help Stripe enable economic infrastructure access for a diverse range of businesses globally.
• Collaborate across teams to incorporate feedback and proactively seek solutions to challenges.
• Rapidly learn new technologies and approaches, demonstrating a strong ability to ask insightful questions and communicate the status of your work effectively.

Who you are

Minimum requirements

• A deep understanding of computer science, obtained through the pursuit of a PhD in Computer Science, Machine Learning, or a closely related field, with the expectation of graduating in winter 2026 or spring/summer 2027.
• Practical experience with programming and machine learning, evidenced by projects, classwork, or research. Familiarity with languages such as Python, Scala, Spark and libraries such as Pandas, NumPy, and Scikit-learn.
• Expertise in areas of machine learning such as supervised and unsupervised learning techniques, ML operations, and possibly experience in Large Language Models or Reinforcement Learning.
• Demonstrated ability to work on collaborative projects, with experience in receiving and applying feedback from various stakeholders.
• A proactive approach to learning unfamiliar systems and a demonstrated ability to understand complex systems independently.
• Intent to return to the degree-program after the completion of the internship/co-op.

Preferred qualifications

You Might Also Have:

• Two years of university education or equivalent experience, with in-depth knowledge in specific domains of machine learning.
• Published and presented peer-reviewed articles in top-tier venues.
• Experience in writing high-quality pull requests, maintaining good test coverage, and completing projects with minimal defects.
• Familiarity with navigating new codebases and managing work across different programming languages.
• Excellent written communication skills to clearly articulate your work to both team members and wider Stripe audiences.

Application requirements

Please submit the following with your application:

• A detailed resume or LinkedIn profile showcasing your work history.
• Examples of relevant work and your approach to learning, such as GitHub repositories, StackOverflow contributions, or other project portfolios.

Join us for an unforgettable summer internship and help shape the future of global commerce. At Stripe, you won't just be working on theoretical projects; you'll make a tangible impact on the world's economic infrastructure.

The role:

The Marketing Data Science (MDS) team is seeking an experienced Machine Learning professional to join the group. This team is essential for supporting SoFi's Marketing organization by providing data-driven decision-making through rigorous analytics, modeling, experimentation, and measurement.

The primary responsibility of this role is to build and scale machine learning models that drive marketing and growth across SoFi's product suite. The focus will be on developing predictive models for key areas: acquisition, conversion, retention, and customer lifetime value (LTV), utilizing behavioral, transactional, and credit data. This role requires strong collaboration across teams to identify the business needs, manage the end-to-end model development cycle, implement the model in production, and monitor performance, while maintaining strict compliance with all regulatory standards.

What you’ll do: 

• Design, develop, and deploy machine learning models to optimize customer acquisition, onboarding, and lifecycle engagement across financial products (e.g., loans, credit cards, money, invest, crypto and etc).
• Build predictive models for critical business outcomes, including Customer Lifetime Value (LTV), conversion propensity, cross-sell/upsell effectiveness, and retention across various channels (e.g., direct mail, email, in-app, Operations).
• Leverage structured and unstructured data (e.g., behavioral signals, transaction data, credit attributes) to drive audience segmentation and personalization at scale. Develop and maintain a robust feature store to accelerate the end-to-end model development lifecycle.
• Build experimentation frameworks to support A/B testing and measure the incremental business impact of the models. Work with the other team members to incorporate the models in marketing/campaign strategies.
• Collaborate with Fair Lending (FL) and Model Risk Management (MRM) to ensure the models and targeting strategies adhere to fair lending laws, privacy regulations, and responsible marketing practices.
• Support the efficient scoring and implementation of models through both real-time and batch processes, ensuring the timely availability of scores for marketing execution and deep analytics.
• Monitor model performance with a focus on business outcomes (conversion, retention, revenue, risk-adjusted returns) and continuously iterate based on insights.
• Partner closely with team members within MDS and key business stakeholders on defining business requirements, prioritizing the model pipeline, providing development updates, and monitoring performance.
• Stay at the forefront of modeling, data, and AI innovations to support scalable marketing growth in financial services.

What you’ll need:

• Bachelor's degree in Computer Science, Math, Physics, Engineering, or a quantitative field required; Master's degree preferred.
• 8+ years of experience in machine learning, data science or applied AI, ideally in marketing, fintech, or consumer tech.
• Strong proficiency in Python, SQL, and ML frameworks (scikit-learn, PyTorch, TensorFlow).
• Familiarity with ML governance, model monitoring, and compliance frameworks (MRM, Fair Lending, etc.) is a plus.
• Experience with LLMs, prompt engineering, and AI application development preferred.
• Demonstrated experience with experimentation and hypothesis testing.
• High intellectual curiosity and a quick learner of new technical skills.
• Ability to initiate and drive projects independently to completion, and ability to clearly communicate technical information to diverse audiences.
• Strong relationship-building and collaborative skills.
• Ability to thrive in a dynamic, cross-functional environment with keen attention to detail.

Polar Operations LLC (dba IMC Markets) has multiple openings for Python Software Engineer (REQ#4461661101) in Chicago, IL and Brooklyn, NY.

Your Core Responsibilities

• Implement software features and components for IMC's trading systems (25%).
• Design software solutions independently and make architectural decisions (10%).
• Debug and fix software issues in trading systems (5%).
• Identify complex multi-component issues, perform root cause analysis, and implement preventative solutions (5%).
• Ensure code quality and system reliability through comprehensive automated testing (10%).
• Contribute to the automated testing framework, own critical trading system components, and ensure reliability, performance, and scalability across regions (5%).
• Participate in code reviews (10%).
• Monitor system performance and trading operations (5%).
• Collaborate with traders and other teams to understand requirements and deliver solutions (10%).
• Participate in planning, estimate complexity of tasks, and help balance technical debt with feature development (5%).
• Mentor and guide junior developers and interns, helping them understand IMC's systems and development practices (10%).

Partial telecommuting permitted. Multiple positions available. Requisition #: 4461661101

Your Skills and Experience

This position requires a Bachelor’s degree, or foreign equivalent, in Computer science, Computer Engineering, or a similarly quantitative and technical field and one (1) year of experience in the job offered or a related occupation.

Must have at least one (1) year of experience in each of the following skill(s):

• Applying analytical and engineering techniques to solve complex problems programmatically;
• Programming with Python;
• Developing and implementing algorithms and data structures;
• Software development lifecycle and best practices;
• Designing and developing software applications in a collaborative, cross-functional environment;
• Experience with DevOps, databases, big data, or frontend frameworks;
• Experience with Docker, Kubernetes, and Linux environments;
• Experience working with financial data;
• Foundational knowledge of quantitative trading concepts and equity/index options;
• Exposure to web/API frameworks such as FastAPI and React;
• Experience with automated testing and CI/CD pipelines;
• Experience collaborating with trading desks and developing software for use in a quantitative trading environment;
• Experience with the following tools: Team City, JIRA ticket management, Gerrit code review tool, GitLab, uv, Gitlab CI, py-spy, memray, and Python Packaging; and
• Experience with the following libraries: Pandas, Polars, NumPy, Tensorflow, Scikit, scikit-learn, cvxpy, and Pytest.

#LI-DNI

Position Summary

We are seeking a skilled Machine Learning Engineer with approximately three years of hands-on experience designing, deploying, and maintaining production-grade machine learning systems. In this role, you will collaborate closely with data scientists, software engineers, and product teams to translate research models into reliable, scalable, and high-impact applications. You will be deeply involved in the end-to-end ML lifecycle—from data ingestion and feature engineering to deployment, monitoring, and continuous improvement—playing a critical part in shaping our machine learning platform and capabilities.

Primary Responsibilities

• Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications.
• Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments.
• Design and implement data pipelines and workflows that support training, inference, and model lifecycle management.
• Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows.
• Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles.
• Leverage cloud platforms (AWS, Azure, GCP) to build scalable ML solutions using managed services and infrastructure-as-code practices.
• Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices.
• Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities.

Qualifications

• Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 6+ years of experience in machine learning engineering, applied ML, or related software engineering roles.
• Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask).
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with CI/CD pipelines, testing automation, and version control using Git.
• Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred.
• Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts.
• Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams.

This is an exempt position. The annualized base pay range for this role is expected to be between $140,000-$180,000.  Actual base pay could vary based on factors including but not limited to experience, subject matter expertise, geographic location where work will be performed, and the applicant's skill set.  The base pay is just one component of the total compensation package for employees.  Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life and 401(k). Please note that the job title is subject to change based on the selected candidate’s experience and education.

THE POSITION
Our roster has an opening with your name on it

At FanDuel, data is the heartbeat of our organization. As a Staff Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will own critical production ML systems across real-time personalization, delivering billions of recommendations per day, and search, powering high-scale, low-latency discovery experiences for customers across our sportsbook and gaming products. You will tackle complex, still-evolving challenges in ranking, relevance, retrieval, and online decisioning to provide our business with insight and our customers with world-class personalized experiences. Every click our users make, every bet, every touchdown, every fumble, and every play is fair game for us to turn into a stream of knowledge. Your expertise will be used here to make better and faster decisions – outpacing our competition.

Collaboration is at the core of your role. You’ll be the linchpin between engineering teams working downstream to build out our online application and upstream to land necessary data for feature engineering. You’ll also be working with Data Scientists and Analysts to productionize, analyze, and validate AI powered insights. You will be asked to help organize, model, and present our data as a coherent product and offer it to our stakeholders, providing a common information framework that allows FanDuel to intelligently react to what is happening on the field and in the marketplace.

We are looking for Staff Machine Learning Engineer who may be looking to make the move to a big data environment. If this describes you, read on – we want to hear from you!

In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

THE GAME PLAN
Everyone on our team has a part to play

• Designing and implementing intelligent search systems incorporating typeahead search, vector search and ML personalization signals to optimize relevance and user experience, with end-to-end ownership from ideation to production
• Building and scaling multi-layer serving architectures for ML and GenAI/LLM models, making key architectural decisions in ambiguous problem spaces
• Driving the design and evolution of platform capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) to streamline ML and GenAI/LLM application development and deployment lifecycle across teams
• Contributing to technical strategy and influencing adoption of ML and GenAI/LLM platform solutions across partner engineering teams
• Applying best practices in data security, privacy (e.g. GDPR, CCPA), governance, and data testing frameworks to ensure reliable and compliant data products
• Owning the continuous integration and delivery of production-grade data and ML systems with a focus on scalability, reliability, and cost-efficiency
• An inclusive culture that expects excellence and prioritizes your growth as an engineer and your well-being as a person
• Advance your career within well-defined, skill-based tracks, either as an individual contributor or as a manager – both providing equal opportunities for compensation and advancement
• Operate as part of an autonomous team with end-to-end ownership of key components of our data and ML platform architecture
• Set engineering standards and mentor junior engineers, elevating team practices in system design, reliability, automation, data quality, and operational excellence
  
  

THE STATS
What we're looking for in our next teammate

Required Qualifications

• 7+ years of relevant experience developing code in one or more core programming languages (Python, Java, etc.)
• 4+ years of experience designing and building scalable software architectures, including systems supporting ML, Search or LLM workloads
• 2+ years of experience implementing vector search, semantic search, or embedding-based retrieval systems (RAG workflows) for production ML or AI applications with vector store such as AWS OpenSearch, Elasticsearch, etc
• 2+ years of experience building platform components and frameworks improving ML/AI development and deployment efficiency
• 1+ years of experience driving technical direction, making architectural trade-offs, and influencing engineering decisions across teams
• 1+ years of experience collaborating cross-functionally, working with leadership and influencing and driving partner teams to adopt platform capabilities and best practices
• 1+ years of experience conducting design reviews and setting engineering standards within a team
• Experience with data and streaming technologies (e.g., Spark, Flink, Kafka, Airflow, Terraform)
• Experience working in cloud environments such as AWS, GCP, or Azure
• Experience designing and building data pipelines for production ML and GenAI/LLM systems
• Strong understanding of data structures, distributed systems, and software engineering principles
• Demonstrated ability to independently own and deliver complex technical projects end-to-end in ambiguous environments
• Ability to communicate technical concepts and insights effectively through dashboards, data models, or design artifacts
• Track record of mentoring engineers and elevating team-wide engineering practices and technical quality

Preferred Qualifications

• 1+ years of experience working with typeahead / autocomplete systems and integrating ML signals into query understanding or ranking workflows
• 1+ years of experience combining outputs from multiple retrieval systems (e.g., vector search + typeahead + personalization models) to improve relevance
• 1+ years of experience deploying ML and GenAI/LLM models under constraints of scalability, correctness, and maintainability in production environments
  • Hands-on experience with ML frameworks (Scikit-learn, PyTorch, TensorFlow, etc.) and familiarity with LLM frameworks
  • Hands-on experience with ML and GenAI/LLM platforms (e.g., SageMaker, Bedrock, Databricks, etc.)
    
    

ABOUT FANDUEL

FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America’s #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry’s unquestioned leader in horse racing and advance-deposit wagering, FanDuel Racing; and its daily fantasy sports product.  

In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV+, its leading direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.

The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.

FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).

PLAYER BENEFITS
We treat our team right

We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO & sick leave), annual bonus and long-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits , pet insurance, and more - check out all our benefits here: FanDuel Total Rewards. *Benefits differ across location, role, and level.

FanDuel is an equal opportunities employer and we believe, as one of our principles states, “We are One Team!”. As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.

The applicable salary range for this position is $159,000 - $208,950 USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short-term or long-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.

FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email Benefits@fanduel.com.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

#LI-Hybrid

THE POSITION
Our roster has an opening with your name on it

We are looking for a Product Manager to join our Data Products team within our Technology. This is a key role in a fast-paced environment working with teams and stakeholders across the business to drive the development and enhancement of our Data Science and Machine Learning products. In this role, you'll drive the evolution and adoption of our ML Platform, including experiment tracking, model lifecycle management, observability, and feature engineering capabilities that enable data scientists to build, deploy, and monitor models at scale. If you’re excited by this challenge and want to work within a dynamic company, then we’d love to hear from you.

In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

THE GAME PLAN
Everyone on our team has a part to play

• You will take ownership of data products that deliver key insights into our business and drive future business decisions
• Lead elicitation of requirements, in the form of user stories & acceptance criteria, prioritizing the product backlog to streamline the execution of program priorities
• Work closely with cross-functional teams, including data scientists, engineers, and business stakeholders, to ensure our platform aligns with business objectives and delivers advanced machine learning solutions
• Partner closely with our Data Science and Machine Learning Data Engineering teams to deliver value through data
• Bring fresh ideas to the table when working to solve business problems, using your commercial understanding to generate innovative solutions
• Partner with Engineering teams to define solution and approach
• Create and maintain user guides, technical documentation, and best practices for the Machine Learning platform, including tooling for experiment tracking, model deployment, feature engineering, and observability
• Monitor platform performance, model accuracy, and data quality. Identify and address issues to continuously improve platform efficiency
• Be an enabling force driving through effective and sustainable change guiding the business through the journey
• Play a key role within the PM community here at FanDuel, sharing your industry best practice and fostering a culture of knowledge sharing and cross-skilling
  
  

THE STATS
What we're looking for in our next teammate

• 3-6 years of experience as a Product Manager, Product Owner, or Data Scientist delivering impactful data products, with a proven track record of successful project delivery and stakeholder satisfaction.
• Experience working with data warehouse and data science technologies, including platforms such as Python, PySpark, Databricks, AWS, and MLflow ensuring efficient data processing and management.
• Proficiency with popular machine learning frameworks and tools, such as TensorFlow, PyTorch, or scikit-learn, encompassing model development, training, and deployment to support advanced analytics and predictive modeling initiatives.
• Expertise in using Databricks for scalable data engineering and machine learning workflows, leveraging its collaborative environment and optimized Spark clusters for accelerated development and deployment cycles.
• Strong proficiency in SQL for querying and manipulating large datasets, combined with advanced programming skills in Python for data preprocessing, feature engineering, and model development tasks.
• Hands-on experience with MLflow for experiment tracking, model registry, and model lifecycle management, including integration with Unity Catalog for centralized governance and versioning
• Familiarity with ML observability and monitoring tools (e.g., Fiddler, Evidently AI, or similar platforms) for tracking model performance, detecting data drift, and ensuring model health in production environments
• Understanding of Infrastructure as Code (IaC) principles and experience with tools such as Terraform for managing ML infrastructure, governance policies, and reproducible deployments
• Extensive experience with feature stores, facilitating efficient feature management and reuse across machine learning models, enhancing model accuracy and reducing time-to-market for data-driven solutions.
• Demonstrated proficiency in a variety of reporting and data visualization tools, such as Tableau, Power BI, or matplotlib, for creating insightful visualizations and dashboards to communicate key insights and drive data-driven decision-making.
• Exceptional analysis skills, with the ability to interpret complex datasets, identify meaningful patterns and trends, and derive actionable insights to inform product strategy and decision-making.
• Proven experience conducting stakeholder interviews and facilitating stakeholder workshops, fostering open communication and collaboration to gather requirements, prioritize features, and align product roadmap with business objectives.
• Proficient in translating product roadmap features into well-defined product requirements, including features, user stories, and acceptance test criteria, ensuring clear understanding and alignment across cross-functional teams.
• Thrives in a fast-paced environment, adapting quickly to changing priorities while driving high standards and delivering value that meets or exceeds challenging goals.
• Drives high standards for individual accomplishment and works to meet or exceed challenging goals, fostering a culture of excellence and continuous improvement within the product development team.
• Is accustomed to working in a fast-paced environment, adapting quickly to changing priorities and requirements, while maintaining a focus on delivering value and driving outcomes for the organization.
• Bachelor's degree in a relevant field preferred (Computer Science, Engineering, Business, or a related discipline). A Master's degree is a plus, providing additional depth and specialization in machine learning, data science, or related fields.
  
  
  

ABOUT FANDUEL

FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America’s #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry’s unquestioned leader in horse racing and advance-deposit wagering, FanDuel Racing; and its daily fantasy sports product.  

In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV+, its leading direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.

The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.

FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).

PLAYER BENEFITS
We treat our team right

We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO & sick leave), annual bonus and long-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits , pet insurance, and more - check out all our benefits here: FanDuel Total Rewards. *Benefits differ across location, role, and level.

FanDuel is an equal opportunities employer and we believe, as one of our principles states, “We are One Team!”. As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.

The applicable salary range for this position is $116,000 - $145,000 USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short-term or long-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.

FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email Benefits@fanduel.com.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

#LI-Hybrid

THE POSITION
Our roster has an opening with your name on it

We are looking for a Product Manager to join our Data Products team within our Technology. This is a key role in a fast-paced environment working with teams and stakeholders across the business to drive the development and enhancement of our Data Science and Machine Learning products. In this role, you'll drive the evolution and adoption of our ML Platform, including experiment tracking, model lifecycle management, observability, and feature engineering capabilities that enable data scientists to build, deploy, and monitor models at scale. If you’re excited by this challenge and want to work within a dynamic company, then we’d love to hear from you.

In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

THE GAME PLAN
Everyone on our team has a part to play

• You will take ownership of data products that deliver key insights into our business and drive future business decisions
• Lead elicitation of requirements, in the form of user stories & acceptance criteria, prioritizing the product backlog to streamline the execution of program priorities
• Work closely with cross-functional teams, including data scientists, engineers, and business stakeholders, to ensure our platform aligns with business objectives and delivers advanced machine learning solutions
• Partner closely with our Data Science and Machine Learning Data Engineering teams to deliver value through data
• Bring fresh ideas to the table when working to solve business problems, using your commercial understanding to generate innovative solutions
• Partner with Engineering teams to define solution and approach
• Create and maintain user guides, technical documentation, and best practices for the Machine Learning platform, including tooling for experiment tracking, model deployment, feature engineering, and observability
• Monitor platform performance, model accuracy, and data quality. Identify and address issues to continuously improve platform efficiency
• Be an enabling force driving through effective and sustainable change guiding the business through the journey
• Play a key role within the PM community here at FanDuel, sharing your industry best practice and fostering a culture of knowledge sharing and cross-skilling
  
  

THE STATS
What we're looking for in our next teammate

• 3-6 years of experience as a Product Manager, Product Owner, or Data Scientist delivering impactful data products, with a proven track record of successful project delivery and stakeholder satisfaction.
• Experience working with data warehouse and data science technologies, including platforms such as Python, PySpark, Databricks, AWS, and MLflow ensuring efficient data processing and management.
• Proficiency with popular machine learning frameworks and tools, such as TensorFlow, PyTorch, or scikit-learn, encompassing model development, training, and deployment to support advanced analytics and predictive modeling initiatives.
• Expertise in using Databricks for scalable data engineering and machine learning workflows, leveraging its collaborative environment and optimized Spark clusters for accelerated development and deployment cycles.
• Strong proficiency in SQL for querying and manipulating large datasets, combined with advanced programming skills in Python for data preprocessing, feature engineering, and model development tasks.
• Hands-on experience with MLflow for experiment tracking, model registry, and model lifecycle management, including integration with Unity Catalog for centralized governance and versioning
• Familiarity with ML observability and monitoring tools (e.g., Fiddler, Evidently AI, or similar platforms) for tracking model performance, detecting data drift, and ensuring model health in production environments
• Understanding of Infrastructure as Code (IaC) principles and experience with tools such as Terraform for managing ML infrastructure, governance policies, and reproducible deployments
• Extensive experience with feature stores, facilitating efficient feature management and reuse across machine learning models, enhancing model accuracy and reducing time-to-market for data-driven solutions.
• Demonstrated proficiency in a variety of reporting and data visualization tools, such as Tableau, Power BI, or matplotlib, for creating insightful visualizations and dashboards to communicate key insights and drive data-driven decision-making.
• Exceptional analysis skills, with the ability to interpret complex datasets, identify meaningful patterns and trends, and derive actionable insights to inform product strategy and decision-making.
• Proven experience conducting stakeholder interviews and facilitating stakeholder workshops, fostering open communication and collaboration to gather requirements, prioritize features, and align product roadmap with business objectives.
• Proficient in translating product roadmap features into well-defined product requirements, including features, user stories, and acceptance test criteria, ensuring clear understanding and alignment across cross-functional teams.
• Thrives in a fast-paced environment, adapting quickly to changing priorities while driving high standards and delivering value that meets or exceeds challenging goals.
• Drives high standards for individual accomplishment and works to meet or exceed challenging goals, fostering a culture of excellence and continuous improvement within the product development team.
• Is accustomed to working in a fast-paced environment, adapting quickly to changing priorities and requirements, while maintaining a focus on delivering value and driving outcomes for the organization.
• Bachelor's degree in a relevant field preferred (Computer Science, Engineering, Business, or a related discipline). A Master's degree is a plus, providing additional depth and specialization in machine learning, data science, or related fields.
  
  
  

ABOUT FANDUEL

FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America’s #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry’s unquestioned leader in horse racing and advance-deposit wagering, FanDuel Racing; and its daily fantasy sports product.  

In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV+, its leading direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.

The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.

FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).

PLAYER BENEFITS
We treat our team right

We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO & sick leave), annual bonus and long-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits , pet insurance, and more - check out all our benefits here: FanDuel Total Rewards. *Benefits differ across location, role, and level.

FanDuel is an equal opportunities employer and we believe, as one of our principles states, “We are One Team!”. As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.

The applicable salary range for this position is $116,000 - $145,000 USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short-term or long-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.

FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email Benefits@fanduel.com.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

#LI-Hybrid

THE POSITION
Our roster has an opening with your name on it

At FanDuel, data is the heartbeat of our organization.  As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will be asked to provide our business with insight and our customers with world-class personalized experiences.  Every click our users make, every bet, every touchdown, every fumble, and every play is fair game for us to turn into a stream of knowledge. Your expertise will be used here to make better and faster decisions – outpacing our competition.

Collaboration is at the core of your role. You’ll be the linchpin between engineering teams working downstream to build out our online application and upstream to land necessary data for feature engineering. You’ll also be working with Data Scientists and Analysts to productionize, analyze, and validate AI powered insights. You will be asked to help organize, model, and present our data as a coherent product and offer it to our stakeholders, providing a common information framework that allows FanDuel to intelligently react to what is happening on the field and in the marketplace.   

We are looking for Senior Machine Learning Engineer who may be looking to make the move to a big data environment.  If this describes you, read on – we want to hear from you!

THE GAME PLAN
Everyone on our team has a part to play

• Designing and implementing intelligent search system incorporating typeahead search, vector search and ML personalization model signals to optimize relevance and user experience
• Contributing to the design and development of scalable serving systems for ML and GenAI/LLM models
• Developing platform features and capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) for streamlining ML Model and GenAI/LLM Application development and deployment lifecycle
• Business intelligence tools (e.g., Tableau, Knime, Looker)
• Data security and privacy (e.g. GDPR, CPP)
• Data governance and data testing frameworks
• Continuous integration and delivery of production data products
• An inclusive culture that expects excellence and priorities your growth as an engineer and your well-being as a person
• Advance your career within well-defined, skill-based tracks, either as an individual contributor or as a manager – both providing equal opportunities for compensation and advancement
• Collaborating with peers and sharing best practices in system reliability, automation, and data quality

ML engineering is a rapidly changing field – most of all, we’re looking for someone who enjoys experimenting, keeping their finger on the pulse of current data engineering tools, and always thinking about how to do something better.

THE STATS
What we're looking for in our next teammate

• 5-7+ years of relevant experience developing code in one or more core programming languages (Python, Java, etc.)
• 1+ Years of Experience implementing vector search, semantic search, or embedding-based retrieval systems for production ML or AI applications
• 1+ Years of Experience working with typeahead / autocomplete systems and integrating ML signals into query understanding or ranking workflows
• 1+ Years of Experience combining outputs from multiple retrieval systems (e.g., vector search + typeahead + personalization models) to improve relevance
• 1+ Years of experience in deploying ML and GenAI/LLM models under the constraints of scalability, correctness, and maintainability.
• Hands on experience with ML frameworks and libraries (Scikit-learn, Pytorch, Tensorflow, LightGBM, Keras, MLFlow etc.) and familiarity with LLM-specific frameworks (e.g., LangChain, Hugging Face Transformers, etc).
• Hands on experience with one or more ML and GenAI/LLM cloud services (Amazon SageMaker, Amazon Bedrock, Databricks Mosaic AI, Seldon, Arize, etc)
• 4+ Years of experience designing and building various software architecture, with some emphasis on scalable architectures supporting both traditional ML and advanced LLM workflows.
• Deep understanding and knowledge of data structures, distributed computing, and software engineering principles
• 3+ Years of experience demonstrating technical leadership working with teams, owning projects, defining, and setting technical direction for projects.
• Experience with one or more relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis, Terraform, Airflow)
• Ability to share findings in easy to consume formats, whether that is through dashboards or data modeling.
• Conduct regular design process reviews and ensure development standards within the team.
• Working with leadership to drive adoption of ML and GenAI/LLM solutions to product engineering teams.
• Experience working in a cloud environment such as AWS, GCP, Azure.
• Experience with Databricks is a plus, their unity catalog, another plus.
• Designing and building data pipelines for production level ML and GenAI/LLM infrastructure.
• Experience with vector databases to efficiently manage and retrieve embeddings for LLM applications, enabling high-performance similarity search and retrieval-augmented generation (RAG) workflows is a plus
• Motivate junior engineers on best practices and latest industry design patterns.

ABOUT FANDUEL

FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America’s #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry’s unquestioned leader in horse racing and advance-deposit wagering, FanDuel Racing; and its daily fantasy sports product.  

In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV+, its leading direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.

The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.

FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).

PLAYER BENEFITS
We treat our team right

We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO & sick leave), annual bonus and long-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits , pet insurance, and more - check out all our benefits here: FanDuel Total Rewards. *Benefits differ across location, role, and level.

FanDuel is an equal opportunities employer and we believe, as one of our principles states, “We are One Team!”. As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.

The applicable salary range for this position is $138,000 - $173,000 USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short-term or long-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.

FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email Benefits@fanduel.com.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

THE POSITION
Our roster has an opening with your name on it

At FanDuel, data is the heartbeat of our organization.  As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will be asked to provide our business with insight and our customers with world-class personalized experiences.  Every click our users make, every bet, every touchdown, every fumble, and every play is fair game for us to turn into a stream of knowledge. Your expertise will be used here to make better and faster decisions – outpacing our competition.

Collaboration is at the core of your role. You’ll be the linchpin between engineering teams working downstream to build out our online application and upstream to land necessary data for feature engineering. You’ll also be working with Data Scientists and Analysts to productionize, analyze, and validate AI powered insights. You will be asked to help organize, model, and present our data as a coherent product and offer it to our stakeholders, providing a common information framework that allows FanDuel to intelligently react to what is happening on the field and in the marketplace.   

We are looking for Senior Machine Learning Engineer who may be looking to make the move to a big data environment.  If this describes you, read on – we want to hear from you!

THE GAME PLAN
Everyone on our team has a part to play

• Designing and implementing intelligent search system incorporating typeahead search, vector search and ML personalization model signals to optimize relevance and user experience
• Contributing to the design and development of scalable serving systems for ML and GenAI/LLM models
• Developing platform features and capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) for streamlining ML Model and GenAI/LLM Application development and deployment lifecycle
• Business intelligence tools (e.g., Tableau, Knime, Looker)
• Data security and privacy (e.g. GDPR, CPP)
• Data governance and data testing frameworks
• Continuous integration and delivery of production data products
• An inclusive culture that expects excellence and priorities your growth as an engineer and your well-being as a person
• Advance your career within well-defined, skill-based tracks, either as an individual contributor or as a manager – both providing equal opportunities for compensation and advancement
• Collaborating with peers and sharing best practices in system reliability, automation, and data quality

ML engineering is a rapidly changing field – most of all, we’re looking for someone who enjoys experimenting, keeping their finger on the pulse of current data engineering tools, and always thinking about how to do something better.

THE STATS
What we're looking for in our next teammate

• 5-7+ years of relevant experience developing code in one or more core programming languages (Python, Java, etc.)
• 1+ Years of Experience implementing vector search, semantic search, or embedding-based retrieval systems for production ML or AI applications
• 1+ Years of Experience working with typeahead / autocomplete systems and integrating ML signals into query understanding or ranking workflows
• 1+ Years of Experience combining outputs from multiple retrieval systems (e.g., vector search + typeahead + personalization models) to improve relevance
• 1+ Years of experience in deploying ML and GenAI/LLM models under the constraints of scalability, correctness, and maintainability.
• Hands on experience with ML frameworks and libraries (Scikit-learn, Pytorch, Tensorflow, LightGBM, Keras, MLFlow etc.) and familiarity with LLM-specific frameworks (e.g., LangChain, Hugging Face Transformers, etc).
• Hands on experience with one or more ML and GenAI/LLM cloud services (Amazon SageMaker, Amazon Bedrock, Databricks Mosaic AI, Seldon, Arize, etc)
• 4+ Years of experience designing and building various software architecture, with some emphasis on scalable architectures supporting both traditional ML and advanced LLM workflows.
• Deep understanding and knowledge of data structures, distributed computing, and software engineering principles
• 3+ Years of experience demonstrating technical leadership working with teams, owning projects, defining, and setting technical direction for projects.
• Experience with one or more relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis, Terraform, Airflow)
• Ability to share findings in easy to consume formats, whether that is through dashboards or data modeling.
• Conduct regular design process reviews and ensure development standards within the team.
• Working with leadership to drive adoption of ML and GenAI/LLM solutions to product engineering teams.
• Experience working in a cloud environment such as AWS, GCP, Azure.
• Experience with Databricks is a plus, their unity catalog, another plus.
• Designing and building data pipelines for production level ML and GenAI/LLM infrastructure.
• Experience with vector databases to efficiently manage and retrieve embeddings for LLM applications, enabling high-performance similarity search and retrieval-augmented generation (RAG) workflows is a plus
• Motivate junior engineers on best practices and latest industry design patterns.

ABOUT FANDUEL

FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America’s #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry’s unquestioned leader in horse racing and advance-deposit wagering, FanDuel Racing; and its daily fantasy sports product.  

In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV+, its leading direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.

The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.

FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).

PLAYER BENEFITS
We treat our team right

We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO & sick leave), annual bonus and long-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits , pet insurance, and more - check out all our benefits here: FanDuel Total Rewards. *Benefits differ across location, role, and level.

FanDuel is an equal opportunities employer and we believe, as one of our principles states, “We are One Team!”. As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.

The applicable salary range for this position is $138,000 - $173,000 USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short-term or long-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.

FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email Benefits@fanduel.com.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

THE POSITION
Our roster has an opening with your name on it

At FanDuel, data is the heartbeat of our organization. As a Staff Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will own critical production ML systems across real-time personalization, delivering billions of recommendations per day, and search, powering high-scale, low-latency discovery experiences for customers across our sportsbook and gaming products. You will tackle complex, still-evolving challenges in ranking, relevance, retrieval, and online decisioning to provide our business with insight and our customers with world-class personalized experiences. Every click our users make, every bet, every touchdown, every fumble, and every play is fair game for us to turn into a stream of knowledge. Your expertise will be used here to make better and faster decisions – outpacing our competition.

Collaboration is at the core of your role. You’ll be the linchpin between engineering teams working downstream to build out our online application and upstream to land necessary data for feature engineering. You’ll also be working with Data Scientists and Analysts to productionize, analyze, and validate AI powered insights. You will be asked to help organize, model, and present our data as a coherent product and offer it to our stakeholders, providing a common information framework that allows FanDuel to intelligently react to what is happening on the field and in the marketplace.

We are looking for Staff Machine Learning Engineer who may be looking to make the move to a big data environment. If this describes you, read on – we want to hear from you!

In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

THE GAME PLAN
Everyone on our team has a part to play

• Designing and implementing intelligent search systems incorporating typeahead search, vector search and ML personalization signals to optimize relevance and user experience, with end-to-end ownership from ideation to production
• Building and scaling multi-layer serving architectures for ML and GenAI/LLM models, making key architectural decisions in ambiguous problem spaces
• Driving the design and evolution of platform capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) to streamline ML and GenAI/LLM application development and deployment lifecycle across teams
• Contributing to technical strategy and influencing adoption of ML and GenAI/LLM platform solutions across partner engineering teams
• Applying best practices in data security, privacy (e.g. GDPR, CCPA), governance, and data testing frameworks to ensure reliable and compliant data products
• Owning the continuous integration and delivery of production-grade data and ML systems with a focus on scalability, reliability, and cost-efficiency
• An inclusive culture that expects excellence and prioritizes your growth as an engineer and your well-being as a person
• Advance your career within well-defined, skill-based tracks, either as an individual contributor or as a manager – both providing equal opportunities for compensation and advancement
• Operate as part of an autonomous team with end-to-end ownership of key components of our data and ML platform architecture
• Set engineering standards and mentor junior engineers, elevating team practices in system design, reliability, automation, data quality, and operational excellence
  
  

THE STATS
What we're looking for in our next teammate

Required Qualifications

• 7+ years of relevant experience developing code in one or more core programming languages (Python, Java, etc.)
• 4+ years of experience designing and building scalable software architectures, including systems supporting ML, Search or LLM workloads
• 2+ years of experience implementing vector search, semantic search, or embedding-based retrieval systems (RAG workflows) for production ML or AI applications with vector store such as AWS OpenSearch, Elasticsearch, etc
• 2+ years of experience building platform components and frameworks improving ML/AI development and deployment efficiency
• 1+ years of experience driving technical direction, making architectural trade-offs, and influencing engineering decisions across teams
• 1+ years of experience collaborating cross-functionally, working with leadership and influencing and driving partner teams to adopt platform capabilities and best practices
• 1+ years of experience conducting design reviews and setting engineering standards within a team
• Experience with data and streaming technologies (e.g., Spark, Flink, Kafka, Airflow, Terraform)
• Experience working in cloud environments such as AWS, GCP, or Azure
• Experience designing and building data pipelines for production ML and GenAI/LLM systems
• Strong understanding of data structures, distributed systems, and software engineering principles
• Demonstrated ability to independently own and deliver complex technical projects end-to-end in ambiguous environments
• Ability to communicate technical concepts and insights effectively through dashboards, data models, or design artifacts
• Track record of mentoring engineers and elevating team-wide engineering practices and technical quality

Preferred Qualifications

• 1+ years of experience working with typeahead / autocomplete systems and integrating ML signals into query understanding or ranking workflows
• 1+ years of experience combining outputs from multiple retrieval systems (e.g., vector search + typeahead + personalization models) to improve relevance
• 1+ years of experience deploying ML and GenAI/LLM models under constraints of scalability, correctness, and maintainability in production environments
  • Hands-on experience with ML frameworks (Scikit-learn, PyTorch, TensorFlow, etc.) and familiarity with LLM frameworks
  • Hands-on experience with ML and GenAI/LLM platforms (e.g., SageMaker, Bedrock, Databricks, etc.)
    
    

ABOUT FANDUEL

FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America’s #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry’s unquestioned leader in horse racing and advance-deposit wagering, FanDuel Racing; and its daily fantasy sports product.  

In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV+, its leading direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.

The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company’s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.

FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).

PLAYER BENEFITS
We treat our team right

We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO & sick leave), annual bonus and long-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits , pet insurance, and more - check out all our benefits here: FanDuel Total Rewards. *Benefits differ across location, role, and level.

FanDuel is an equal opportunities employer and we believe, as one of our principles states, “We are One Team!”. As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.

The applicable salary range for this position is $159,000 - $208,950 USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short-term or long-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.

FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email Benefits@fanduel.com.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

#LI-Hybrid

WHAT YOU'LL DO

As our customer base continues to grow with the excitement around BrazeAI, we’re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:

• Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
• Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
• Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
• Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
• Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success

WHO YOU ARE

• Education: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master’s or PhD in a relevant technical discipline preferred
• Experience: 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
• Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
• Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
• Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms
• Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
• Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
• Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
• Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $98,000-$164,000/year, with an expected On Target Earnings (OTE) between $109,000-$183,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid

WHAT YOU'LL DO

As our customer base continues to grow with the excitement around BrazeAI, we’re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:

• Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
• Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
• Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
• Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
• Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success

WHO YOU ARE

• Education: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master’s or PhD in a relevant technical discipline preferred
• Experience: 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
• Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
• Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
• Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms
• Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
• Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
• Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
• Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $98,000-$164,000/year, with an expected On Target Earnings (OTE) between $109,000-$183,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid

WHAT YOU'LL DO

As our customer base continues to grow with the excitement around BrazeAI, we’re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:

• Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
• Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
• Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
• Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
• Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success

WHO YOU ARE

• Education: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master’s or PhD in a relevant technical discipline preferred
• Experience: 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
• Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
• Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
• Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms
• Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
• Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
• Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
• Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $98,000-$164,000/year, with an expected On Target Earnings (OTE) between $109,000-$183,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid

WHAT YOU'LL DO

As our customer base continues to grow with the excitement around BrazeAI, we’re expanding our team! Join our Forward-Deployed Data Scientist group of creative technical experts who partner with customers to ensure their success. In this role, you will:

• Collaborate with customer Analytics/BI teams and Braze colleagues on implementations, including use case definition, data integration, pipeline setup, and ML model configuration
• Extend product capabilities by improving architecture and developing reusable data pipelines, APIs, and components
• Work closely with the RL pipeline development team to refine and advance our reinforcement learning (self-learning) algorithms
• Contribute to shaping BrazeAI product strategy and roadmap through customer-facing insights and technical expertise
• Provide ongoing technical expertise to ensure successful adoption, measurable outcomes, and long-term customer success

WHO YOU ARE

• Education: Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field required; Master’s or PhD in a relevant technical discipline preferred
• Experience: 3–5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments. Experience in customer-facing or consulting roles is strongly preferred
• Strong technical expertise: Proficient in Python (Pandas) and core ML libraries (TensorFlow, Keras, scikit-learn, CatBoost, XGBoost). Skilled in SQL for querying/manipulating datasets, with experience in machine learning pipelines and model deployment
• Engineering best practices: You write well-structured, modular, documented code; follow strong development practices (Git, CI/CD, testing frameworks, type-hinting, code reviews); and can build scalable, maintainable solutions
• Nice-to-have skills: Experience with DevOps tools (Airflow, Kubernetes, Terraform, GCP), data integration/ETL, and pipeline optimization, or reinforcement learning algorithms
• Customer collaborator: Comfortable working directly with clients and cross-functional teams, aligning stakeholders, and translating technical concepts into clear business value
• Entrepreneurial problem-solver: You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions
• Continuous learner: You stay current with industry trends, explore new tools/technologies, and thrive in environments that push you to grow
• Clear communicator: Able to explain complex technical ideas persuasively to both technical and non-technical audiences

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $98,000-$164,000/year, with an expected On Target Earnings (OTE) between $109,000-$183,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.

#LI-Hybrid

The role:

We're looking for a Senior Manager, Data Science/ML who will drive CookUnity's next phase of product innovation through forward-looking data science capabilities. This role goes beyond traditional analytics—you'll be responsible for building the ML and experimentation foundation that enables personalized, intelligent product experiences at scale.

As Senior Manager, you'll own the strategic vision for how data science shapes our product roadmap. You'll build and lead a team focused on predictive modeling, personalization, experimentation frameworks, and emerging ML capabilities that directly impact customer engagement, retention, and lifetime value. This is a high-impact role for someone who thinks strategically about the future of product science while remaining hands-on in driving technical execution.

Responsibilities:

Strategic Vision & Product Partnership

• Define and execute the product data science strategy, identifying opportunities where ML and predictive analytics can unlock step-change improvements in customer experience and business outcomes
• Partner closely with Product, Growth, Engineering, and UX leadership to influence product roadmap with data-driven insights and forward-looking ML capabilities
• Act as a thought leader on emerging data science techniques (personalization, recommendation systems, causal inference, generative AI) and their application to product problems
• Translate complex product challenges into clear data science problems with measurable success criteria
• Own the end-to-end ML lifecycle for product use cases: problem framing, feature development, model training, deployment, monitoring, and iteration
• Partner with the Growth Data Science & Analytics team to align experimentation, measurement, and modeling efforts into a cohesive end-to-end data science ecosystem.

Team Leadership & Development

• Build, mentor, and scale a high-performing product data science team capable of delivering both strategic insights and production ML systems
• Foster a culture of innovation, experimentation, and continuous learning within the data science organization
• Create career development pathways that attract and retain top data science talent
• Collaborate with Analytics Engineering to ensure seamless model deployment and monitoring

Advanced Analytics & ML Capabilities

• Own and evolve personalization and recommendation systems that drive engagement and conversion across the customer journey
• Design and implement robust experimentation frameworks that enable rapid, high-quality product testing and learning
• Develop causal inference methodologies to understand true incrementality of product changes.
• Ensure models are observable, explainable where needed, and continuously improved post-launch

Product Measurement & Impact

• Define product success metrics and measurement frameworks that align with business objectives
• Build scalable dashboards and monitoring systems that provide real-time visibility into product performance
• Conduct deep-dive analyses on user behavior patterns to uncover opportunities for product optimization

Qualifications:

• 8+ years of experience in data science, with at least 3 years in leadership roles managing data scientists or ML engineers
• Proven track record building and deploying ML models in production, particularly in personalization, recommendation systems, or predictive modeling
• Deep expertise in experimentation and causal inference, including A/B testing, incrementality measurement, and statistical rigor
• Strong product sense and business acumen—ability to identify high-impact opportunities and translate them into data science initiatives
• Experience in consumer tech, e-commerce, or marketplace businesses where personalization and user engagement are critical
• Excellent communication skills—ability to explain complex technical concepts to non-technical stakeholders and influence product strategy
• Hands-on technical proficiency in Python, SQL, and modern ML frameworks (scikit-learn, PyTorch, TensorFlow)
• Experience with cloud-based data infrastructure (AWS, GCP, Snowflake) and ML Ops tools (MLflow, Airflow, Kubeflow)

Preferred requirements:

• PhD or Master's degree in Computer Science, Statistics, Mathematics, or related quantitative field
• Experience with real-time ML systems and feature stores
• Background in recommendation systems or two-tower/multi-modal embeddings
• Familiarity with generative AI and LLM applications in product contexts
• Experience building data science teams from scratch or through periods of rapid growth
• Prior work in subscription businesses or retention-focused products
• Knowledge of modern product analytics tools (Amplitude, MixPanel, Looker)

What Success Looks Like

• 6 months: Established product data science roadmap aligned with business priorities; shipped at least one high-impact ML model to production; built strong partnerships with Product and Engineering leadership
• 12 months: Scaled the product data science function with key hires; delivered measurable improvements in personalization and customer engagement metrics; implemented robust experimentation frameworks used across product teams

Learn More About CookUnity

We believe great leadership starts with alignment on vision, values, and ways of working. To give you deeper insight into who we are and what we’re looking for, we invite you to explore: CookUnity's Leadership Principles – The values and behaviors that guide how we operate, collaborate, and scale.

We hope this provides valuable insight into our culture and product vision. If this excites you, we’d love to connect!

Benefits

🩺  Health Insurance coverage

🌅 401k Plan

📈 We grow, you grow: Stock Options Plan granted on Day 1

🌟 Eligible for a bi-annual performance bonus

⛱ Unlimited PTO

🗓️ 5- year Sabbatical: After 5 years with CookUnity, you get a 4-week paid sabbatical

🐣 Paid Family leave

🕯 Compassionate Leave: 3-5 days each time the need arises

🥘 A generous amount of CookUnity credits to enjoy our amazing meals, added to your account, monthly

🧘🏽‍♀️ Wellness perks: access to fitness subsidies to build a healthy lifestyle

🤖 AI-forward workplace: enterprise access to ChatGPT and Claude to help you work smarter and grow faster.

👩🏾‍🏫 Personalized Spanish coach

🚀 Awesome opportunity to join a company that is looking to change how we eat and how chefs work!

As a Hunter Solutions Architect on the Digital Natives team, you will shape the future of the big data landscape by working with the most sophisticated data engineering and data science teams in the world.

Reporting to the Field Engineering Manager, you will collaborate with customers, product teams, and the broader customer-facing team to develop architectures and solutions using our platform and APIs. You will guide customers through the competitive landscape, best practices, and implementation; and develop technical champions along the way.

The impact you will have:

• You will partner with the sales team and provide technical leadership to help customers understand how Databricks can help solve their business problems.
• Work directly with prospective, greenfield, customers on their 0 to 1 journey with Databricks 
• Consult on Big Data architectures, implement proof of concepts for strategic projects, spanning data engineering, data science and machine learning, and SQL analysis workflows. As well as validating integrations with cloud services, home grown tools, and other 3rd party applications
• Collaborate with your fellow Solutions Architects, using your skills to support each other and our users
• Become an expert in, promote, and recruit contributors for Databricks inspired open-source projects (Spark, Delta Lake, and MLflow) across the developer community.

What we look for:

• 5+ years in a data engineering, data science, technical architecture, or similar pre-sales/consulting role
• Experience building distributed data systems at scale 
• Comfortable programming in, and debugging, Python and SQL
• Experience using agentic coding harnesses and demonstrable experience scaling your work leveraging them 
• Have built solutions with public cloud providers such as AWS, Azure, or GCP
• Expertise in one of the following:
• Data Engineering technologies (Ex: Spark, Hadoop, Kafka)
• Data Science and Machine Learning technologies (Ex: pandas, scikit-learn, pytorch, Tensorflow)
• Available to travel to customers in your region
• [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
• Nice to have: Databricks Certification

NOTE: If your experience isn’t a perfect match for the job description, but you feel like you’re a good fit for the role, apply.  We want to work with smart, passionate, people who are good at their job, and, a lot of times, those people have non-linear career paths.

Senior Data Scientist

About Current

Current is a leading U.S. fintech serving people who have been overlooked by traditional banks. We are one of the fastest-growing companies with nearly 6 million members. Headquartered in NYC, our results-driven environment empowers our team to build better products, grow faster, and have a direct impact on our mission to improve financial outcomes for our members.

About the Role

At Current, data is at the core of everything we do. As a Senior Data Scientist on the Data and Growth teams, you will focus on machine learning modeling to build predictive models that help us understand our customers better, improve user experience, and efficiently acquire and retain members. This is a hands-on role focused on solving complex problems, developing and deploying ML models, and driving data-driven decision-making.

Your day-to-day will involve analyzing, preparing, and structuring data; building and optimizing predictive models; leveraging clustering algorithms; and collaborating with cross-functional teams (Product, Engineering, Marketing) to integrate ML solutions into customer-focused strategies and processes.

Responsibilities

• Design, develop, and deploy predictive classification and regression models, along with anomaly detection models and algorithms.
• Conduct extensive EDA, feature engineering, and data preprocessing to ensure high-quality input for ML models.
• Evaluate and optimize model performance using statistical and ML techniques.
• Design and execute A/B tests to measure and validate model impact.
• Perform customer segmentation using various clustering techniques.
• Develop and implement model monitoring dashboards and establish model governance techniques.
• Collaborate with Analytics, Product, Engineering, and Marketing teams to seamlessly integrate predictive models into workflows.
• Work with the ML Engineering team to ensure efficient data pipelines and scalable model deployment.
• Analyze diverse datasets to extract meaningful insights and patterns, identifying actionable opportunities for optimization and innovation.

Qualifications

About You

• 5+ years of experience in data science with a strong emphasis on machine learning modeling.
• Proficiency in Python for data analysis and ML, with experience using libraries such as Scikit-Learn, XGBoost, TensorFlow, or PyTorch.
• Expertise in SQL and working with large, complex structured and semi-structured datasets.
• Strong understanding of core machine learning techniques, including logistic regression, gradient boosting, decision trees, and clustering methods.
• Experience in feature engineering, model selection, and performance optimization.
• Experienced in designing, executing, analyzing, and reporting on experiments.
• Strong communication skills and ability to present findings to technical and non-technical stakeholders.
• Master’s or higher degree in Data Science, Computer Science, Statistics, or a related field is preferred.

Preferred Skills & Qualifications

• Experience working in fintech, e-commerce, or other data-rich consumer-facing industries.
• Familiarity with Google Cloud Platform (GCP) services, particularly Vertex AI and Dataflow, for scalable data processing and model training.
• Experience with dbt for data modeling.
• Familiarity with BigQuery or other MPP (Massively Parallel Processing) databases.
• Experience using the Scala programming language for developing scalable and efficient data pipelines.

Benefits

• Competitive salary
• Stock options
• 401(k) savings plan
• Discretionary performance bonus program
• Biannual performance reviews
• Medical, Dental and Vision premiums covered at 100% for you and your dependents
• Unlimited time off and paid holidays
• Generous parental leave policy
• Commuter benefits
• Healthcare and Dependent care FSA benefit
• Employee Assistance Programs focused on mental health
• Healthcare advocacy program for all employees
• Access to mental health apps
• Team building activities
• Our modern Chelsea-based office with open floor plan, stocked kitchen, and catered lunches

This role has a base salary range of $170,000.00 - $220,000.00. Compensation is determined based on experience, skill level, and qualifications, which are assessed during the interview process. Current offers a competitive total rewards package which includes base salary, equity, and comprehensive benefits.

We are seeking a highly skilled and experienced Technical AI Project Manager to lead and oversee the planning, execution, and successful delivery of software and AI development projects. The ideal candidate will have a strong background in managing multiple complex software development initiatives, ensuring they are completed on time, within scope, and on budget. A strong technical foundation, exceptional organizational skills, and the capacity to communicate effectively across technical and non-technical teams are essential for this position.

Department 

The Technology Division at Success Academy is the chief provider of technology infrastructure, application services, security, and support. The Division partners with teams across the organization to equip students, teachers, leaders, and staff with the innovative technology tools and know-how necessary to achieve academic and operational excellence.

The Technical AI Project Manager will be a key contributor to our mission of driving innovation across the organization. This role will be responsible for building and managing end-to-end project plans, monitoring and reporting progress, effectively managing scope and dependencies, proactively mitigating risks, and ensuring that projects are delivered within time, cost, and quality specifications. This role will be part of a highly collaborative team of professionals with a wide range of skills, including DevOps engineering, data engineering, software engineering, RPA engineering, and project management.

About the Role

• Acts as project lead, managing cross-functional teams with responsibilities for project schedule and performance.
• Ensures that business outcomes are defined, approved, and delivered. Be responsible for benefits realization and ensure that benefits are delivered in the best way possible.
• Supports strong governance, aligned with the overall governance, and ensures that timely decisions are taken.
• Communicates regularly and effectively the project roadmap and milestones with project executive stakeholders.
• Directly contributes to project definition, development, and/or enhancement efforts, including cost management/avoidance, schedule estimation and tracking, contract performance management, risk management, requirements analysis, and quality assurance activities.
• Performs analysis of business and user needs, documentation of requirements, and translation into proper requirements specifications.
• Utilizes expert communication skills needed to direct skilled technical resources and report on the technical progress, issues, and problem areas, as well as write and review technical documents.
• Answers technical questions regarding products and services and may take part in putting together proposals, configurations, and product plans.
• Prepares affected business areas for transition to a new way of working, potentially implementing new business processes.

Responsibilities

• Lead the end-to-end planning, execution, and delivery of software development and AI/ML projects.
• Collaborate with stakeholders to define project scope, goals, and deliverables that support business objectives, specifically translating business needs into technical requirements for software and AI solutions.
• Create and maintain detailed project plans, schedules, and roadmaps, including technical milestones for development, testing, and deployment phases.
• Monitor and track project progress, adjusting plans as necessary to ensure technical milestones are met and dependencies are managed effectively.
• Identify and mitigate technical risks (e.g., scalability issues, data quality, model performance, integration challenges) and resolve issues to keep projects on track.
• Facilitate communication and collaboration between cross-functional teams (software engineering, data science, AI/ML engineering, design, QA, product).
• Provide regular project status updates to stakeholders and upper management, clearly articulating technical progress and potential roadblocks.
• Manage project budgets, timelines, and resource allocations for technical teams.
• Ensure that all project documentation, including technical specifications, architecture diagrams, and model documentation, is complete, up-to-date, and accessible.
• Foster an agile development environment, leading sprint planning, daily standups, and retrospectives, with a focus on technical sprint goals and deliverables.
• Ensure that the development process aligns with best practices in software engineering, data governance, and responsible AI development.
• Drive continuous improvement of project management processes and tools, integrating best practices for technical project delivery.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Business Management, or a related field.
• 5+ years of experience in software project management, ideally in a mid-to-senior capacity, with a proven track record of delivering complex technical projects.
• Proven experience managing full software development life cycles (SDLC) from concept to deployment.
• Strong understanding of software development methodologies (Agile, Scrum, Kanban, or Waterfall).
• PMP, PRINCE2, or Scrum Master certification preferred.
• Experience working with tools such as Jira, Asana, Trello, Confluence, Monday.com, or similar project management software.
• Strong business analysis skills, with a focus on working with a diverse group of users to understand and solve problems.
• Ability to manage multiple projects simultaneously and prioritize tasks in a fast-paced, technically demanding environment.
• Strong problem-solving skills with the ability to make informed technical decisions under pressure.
• Proficiency in budgeting, forecasting, and resource management for engineering teams.
• Experience working with remote teams and managing distributed projects is a plus.
• Familiarity with software development practices and technical concepts, including cloud infrastructure, APIs, and version control (e.g., Git).

Desired Skills

• Experience in managing projects in cloud environments (AWS, Azure, GCP), including an understanding of cloud-native services for compute, storage, and data.
• Knowledge of DevOps practices and CI/CD pipelines, with an understanding of how to optimize release cycles for software and AI models.
• Familiarity with software testing and quality assurance processes, including automated testing and performance testing.
• Strong organizational skills with attention to detail, particularly in managing complex technical dependencies.
• Any candidate who has been part of the development team and has spent time as a developer is ideal for this role and is desired.

Technical Skills for Software Development

• Understanding of common programming languages such as Python, Java, JavaScript, C#, or Go.
• Familiarity with database technologies (e.g., SQL, PostgreSQL, MongoDB, Cassandra).
• Knowledge of front-end and back-end frameworks (e.g., React, Angular, Node.js, Spring Boot, Django, Flask).
• Experience with API design, development, and integration (RESTful, GraphQL).
• Understanding of microservices architecture and distributed systems.
• Familiarity with containerization technologies (Docker, Kubernetes).
• Knowledge of security best practices in software development (e.g., OWASP Top 10).

Technical Skills for AI Development

• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Familiarity with AWS Bedrock and its available foundation model.
• Understanding of data science concepts, including statistical modeling, data preprocessing, feature engineering, and model evaluation metrics.
• Experience with data pipelines and ETL processes for machine learning (e.g., Apache Spark, Kafka, Talend).
• Knowledge of MLOps practices for deploying, monitoring, and managing machine learning models in production.
• Familiarity with natural language processing (NLP) or computer vision (CV) concepts if relevant to the projects.
• Understanding of responsible AI principles, including fairness, interpretability, and privacy.
• Experience with big data technologies and data warehousing solutions.

About impact.com
impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products - Performance (affiliate), Creator (influencer), and Advocate (customer referral) - unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands - including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results.

Your Role at impact.com:

We're seeking a Senior Data Scientist to serve as an embedded Data Scientist within our Programmatic Experience Group. You'll own the design and deployment of machine learning models that optimize yield, pricing, and inventory allocation at scale — sitting at the intersection of data science, platform engineering, and marketplace economics.

This is a high-craft, high-ownership individual contributor role. You'll work end-to-end: architecting the data pipelines that feed your models, engineering the features that drive performance, and deploying real-time inference systems that make decisions at speed. Your work directly determines how effectively Impact's programmatic marketplace balances advertiser performance with publisher monetization — making this one of the highest-leverage technical roles in the business.

You'll collaborate closely with Product, Data Science, and Programmatic Delivery Engine Engineering, but you operate with significant autonomy. You're expected to bring both the modeling rigor of a data scientist and the production instincts of an ML engineer — and to be genuinely excited about both.

What You'll Do:

Yield Optimization & Pricing Models

• Design and deploy ML models that optimize auction pricing, bid shading, floor price setting, and yield across Impact's programmatic inventory.
• Build and iterate on real-time pricing algorithms that balance short-term revenue efficiency with long-term publisher and advertiser health.
• Develop and maintain feedback loops that allow pricing models to adapt to shifting market conditions, inventory mix, and demand patterns.
• Quantify the revenue impact of pricing model improvements; communicate tradeoffs between yield maximization, fill rate, and partner ROI to stakeholders.

Inventory Allocation & Supply Optimization

• Own ML-driven inventory allocation logic: routing, pacing, and matching supply to demand across partner segments, deal types, and campaign objectives.
• Build models that forecast inventory availability, demand curves, and clearing prices to support proactive allocation decisions.
• Identify and address inefficiencies in inventory utilization — including unsold inventory, suboptimal deal matching, and allocation imbalances across the publisher base.

Data Architecture & Feature Engineering

• Design and own the data infrastructure that feeds programmatic models: event pipelines, feature stores, training datasets, and real-time feature serving.
• Engineer high-signal features from auction logs, bid stream data, user signals, contextual attributes, and historical performance — at the scale of programmatic data volumes.
• Build robust data pipelines with production-grade standards: reliability, observability, versioning, and efficient reprocessing.

Real-Time Inference & Production ML

• Deploy models to production real-time inference environments; own latency, reliability, and throughput requirements for auction-time decision-making.
• Build monitoring systems that track model performance, data drift, and system health in production; define alerting thresholds and retraining triggers.
• Partner with MLOps and Platform Engineering to ensure scalable, low-latency serving infrastructure meets SLOs under high-volume auction traffic.
• Own the full model lifecycle: training, evaluation, deployment, A/B testing, and iteration.

Experimentation & Performance Measurement

• Design and execute rigorous A/B and holdout experiments to measure the causal impact of model changes on yield, fill rate, advertiser performance, and publisher revenue.
• Build evaluation frameworks that go beyond offline metrics — validating model behavior in live auction environments where feedback signals are delayed or noisy.
• Translate experimental results into clear business narratives; present findings and recommendations to Product and business stakeholders.

Self-Learning Systems & Feedback Loops

• Research and implement adaptive, self-learning components within the programmatic stack — including contextual bandits, reinforcement learning signals, and online learning approaches where appropriate.
• Design feedback mechanisms that close the loop between auction outcomes, model updates, and system behavior; reduce reliance on manual tuning and rule-based overrides.
• Stay current with advances in programmatic ML, auction theory, and online optimization; evaluate applicability to Impact's specific marketplace dynamics.

Cross-Functional Collaboration

• Serve as the primary ML technical partner for the Rubicon product and engineering teams; translate business requirements into modeling approaches and communicate technical tradeoffs clearly.
• Collaborate with Data Science peers on shared infrastructure, modeling standards, and cross-domain feature reuse.
• Document models, architectures, and experimental findings to a standard that enables review, replication, and knowledge transfer across teams.

What You Bring:

Required

• Experience: 5+ years in data science, ML engineering, or quantitative research, with at least 2+ years building and deploying ML models in programmatic advertising, ad tech, marketplace optimization, or a closely related domain (e.g., real-time bidding, dynamic pricing, auction systems).
• Programmatic & marketplace depth: Demonstrated understanding of programmatic auction mechanics (RTB, header bidding, floor pricing, deal types, bid shading) and how ML can be applied to optimize outcomes across the supply-demand stack.
• Production ML engineering: Proven ability to take models from prototype to production independently — including real-time inference, monitoring, retraining pipelines, and SLO ownership.
• Data architecture: Experience designing and building data pipelines, feature stores, and training infrastructure for high-volume, low-latency ML systems.
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and large-scale data tools (Spark, Kafka, or equivalent streaming/batch frameworks).
• Experience with real-time feature serving and low-latency model deployment (REST APIs, gRPC, or streaming inference).
• Familiarity with production ML workflows: model versioning, drift monitoring, A/B testing, evaluation, and retraining.
• Experience processing and modeling at programmatic data scale: high-cardinality auction logs, bid stream data, impression and click events.
• Experimentation rigor: Strong grasp of causal inference and experiment design in online, delayed-feedback environments (auction holdouts, switchback tests, variance reduction techniques).
• Communication: Ability to explain complex modeling decisions and tradeoffs to Product and business stakeholders; comfortable presenting in cross-functional forums.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, Economics, or similar); Master's/PhD preferred.

Preferred / Nice to Have

• Direct experience with SSP, DSP, or exchange-side yield optimization — particularly floor price optimization, bid landscape modeling, or deal matching algorithms.
• Familiarity with auction theory (first-price vs. second-price dynamics, optimal reserve pricing, revenue equivalence) and its practical implications for programmatic ML.
• Experience with contextual bandits, multi-armed bandits, or reinforcement learning applied to real-time decisioning problems.
• Knowledge of online learning and adaptive algorithms in production environments with non-stationary data distributions.
• Familiarity with privacy-preserving ML techniques relevant to programmatic (differential privacy, federated learning, cookieless attribution modeling).
• Experience with GCP tools (BigQuery, Vertex AI, Dataflow, Pub/Sub) and/or Databricks/Spark for large-scale event processing and model training.
• Exposure to supply forecasting, inventory management, or capacity planning in programmatic or marketplace contexts.
• Familiarity with Impact's affiliate and partnership ecosystem, or prior experience at the intersection of performance marketing and programmatic delivery.

What Sets You Apart

• Marketplace intuition. You understand programmatic auctions not just as an engineer but as an economist — you think about incentive structures, equilibrium dynamics, and how model decisions ripple through the supply-demand stack.
• Full-stack ML ownership. You're as comfortable designing a feature store schema as you are tuning a gradient boosting model or debugging a latency spike in production. You own the whole chain.
• Feedback loop thinking. You don't just deploy models — you design the systems that make them smarter over time. You think about how today's decisions become tomorrow's training signal.
• Rigor under real-world constraints. You know how to run clean experiments in environments where feedback is delayed, data is noisy, and business pressures create tradeoffs. You don't let imperfect conditions become an excuse for imprecise thinking.
• Pragmatic delivery. You ship. You balance the perfect with the production-ready, iterate fast, and know when an MVP outperforms a six-month research project.
• Collaborative depth. You build genuine technical trust with engineering and product partners — not just by having good ideas, but by following through, communicating clearly, and making the integration easy.

Salary Range: $165,000 - $185,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.

*This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Medical, Dental, and Vision insurance
• Office-only catered lunch every Thursday, a healthy snack bar, and great coffee to keep you fueled 
• Flexible spending accounts and 401(k)
• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses. 

impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

#LI_NewYork_NewYork

Data at Brex

The Data organization develops insights, models, and data infrastructure for teams across Brex, including Sales, Marketing, Product, Engineering, and Operations. Our Data Scientists, Analysts, and Engineers work together to make data—and insights derived from data—a core asset across the company.

What you’ll do

As a senior Data Analyst (DA III), you will own the end-to-end analytics lifecycle for one or more business areas at Brex. You’ll go beyond building dashboards—you’ll frame the right questions, design rigorous analyses, apply statistical methods, and translate your findings into clear recommendations for leadership. You will also serve as a technical leader on the Data Analytics team, mentoring more junior analysts and helping define the standards and best practices that elevate the team’s work.

This role sits at the intersection of analytics, analytics engineering, and business strategy. You’ll work in a modern data stack environment and partner closely with Data Scientists, Data Engineers, and senior leaders across the organization.

Where you’ll work

This role will be based in our New York office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Own the analytics lifecycle for assigned business areas: from problem framing and data sourcing through analysis, insight generation, and stakeholder presentation.
• Build and maintain dashboards and self-service reporting tools that enable business teams to independently track performance, identify risks, and make data-driven decisions.
• Write production-quality SQL and Python code to extract, transform, and analyze data at scale.
• Collaborate with Data Engineers and Data Scientists to develop and maintain analytical data models, improve data pipelines, and ensure data quality across the organization.
• Partner with leadership across Sales, Operations, Product, Finance, and other departments to identify high-impact analytical opportunities and deliver actionable recommendations.
• Mentor other data analysts and contribute to the development of team standards, documentation, code review practices, and analytical frameworks.
• Proactively identify gaps in data infrastructure, propose improvements, and contribute to the evolution of the team’s tooling and processes.

Requirements

• 5+ years of experience in data analytics, business intelligence, or a related quantitative role.
• 3+ years of experience partnering directly with Sales, Operations, Product, or equivalent business teams as an embedded analytics partner.
• Advanced SQL proficiency, including CTEs, window functions, performance optimization, and working across complex data models.
• Proficiency in Python for data analysis, automation, and modeling (Pandas, NumPy, scikit-learn, or similar).
• Experience with cloud data warehouses, particularly Snowflake (BigQuery and Databricks also valued).
• Hands-on experience with BI and data visualization tools (Looker, Tableau, Hex, or similar).
• Strong stakeholder management skills—proven ability to present complex technical findings to non-technical audiences.
• Experience with generative AI and LLM-based tools (Claude Code, Cursor, GitHub Copilot) to perform and accelerate analyses, automated reporting, and build self-service data tools.

Bonus points

• Demonstrated experience applying statistical methods to business problems (e.g., regression, classification, A/B testing).
• Experience with dbt for data modeling and transformation.
• Experience building and maintaining data pipelines using orchestration tools such as Airflow.
• Experience working with APIs for data ingestion and integration.
• Familiarity with version control systems (Git).
• Experience in fintech, financial services, or payments.
• Track record of leading cross-functional analytics projects from scoping through delivery.

Compensation

The expected salary range for this role is $114,192 - $142,740. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

Role Description

• As a Senior Value Engineer specializing in Healthcare Distribution and Logistics, you are pushing the envelope in solving business-critical problems for one of the world's largest healthcare wholesale and distribution enterprises. You will be working intimately with this strategic client, understanding their uniquely complex objectives—spanning from high-volume warehouse optimization and pharmacy fulfillment to ensuring seamless hospital supply logistics and DSCSA compliance—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners (e.g., Microsoft, OpenAI, Databricks).

  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the intricate realities of our customer's massive, high-velocity distribution network. This enables them to industrialize AI, unlocking real ROI on deployments at scale, and ultimately ensuring critical medicines and medical supplies reach care providers and patients without delay. There is no AI without PI. You will prototype these solutions, demonstrate their value to global Supply Chain SVPs and logistics leaders, and ensure successful implementation, adoption, and value realization to solidify and expand the footprint of Celonis within this premier healthcare distribution network.

Key Responsibilities:

• AI Discovery & Solutioning: Understand the client's overarching AI strategy and the distinct supply chain challenges inherent to healthcare wholesale (e.g., predictive demand forecasting for generic and brand pharmaceuticals, optimizing distribution center throughput, navigating complex contract pricing, or managing acute care logistics). As a Celonis product and distribution domain expert, translate these high-volume logistics requirements into innovative AI solutions that drive measurable impact.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the expansion/pre-sales cycle across various distribution and operational business units. Remain deeply involved post-sale to guide implementation, ensuring agreed value, efficiency, and adoption thresholds are successfully reached in both corporate and warehouse environments.

• Hackathons & Prototyping: Think out of the box with a "can-do" attitude, tackling heavily siloed legacy logistics and fulfillment networks. Leverage cutting-edge AI technologies to rapidly build creative prototypes in client hackathons, solving critical pain points across inventory allocation, fleet routing, procurement, and customer service.

• Agentic Process Transformation: Support the client in achieving tangible ROI from AI at an enterprise scale. Enable a fundamental shift from traditional, rule-based automation to autonomous AI agents empowered by the Celonis Process Intelligence Platform (e.g., autonomous resolution of order-to-cash disputes, intelligent backorder handling, or automated warehouse exception management).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. Architect and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails, ensuring seamless integration with complex enterprise ERPs, Warehouse Management Systems (WMS), Transportation Management Systems (TMS), and strict regulatory track-and-trace frameworks (e.g., DSCSA).

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for Healthcare Distribution, scaling knowledge across the organization regarding the nuances of medical/surgical and pharmaceutical wholesale operations.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within highly complex Supply Chain, Logistics, or Wholesale Distribution environments (Healthcare/Pharma distribution preferred). This includes defining AI roadmaps, building compelling ROI/TCO business cases for large-scale distribution networks, and guiding technical implementations through to value realization.

• Deep understanding of supply chain business processes native to high-volume distribution (such as Order-to-Cash, Procure-to-Pay, Inventory Management, Warehouse Operations, or Transportation Logistics) with the ability to translate high-level operational needs into specific AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent chatbots for pharmacy/hospital customer support, or automated extraction of data from complex vendor contracts and rebate agreements).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies for handling massive, high-velocity transactional supply chain datasets.

• Strong presentation skills to both internal and external stakeholders (including supply chain executives, warehouse directors, and enterprise IT leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, supply chain management, logistics, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations suited for highly regulated healthcare logistics environments.

• Familiarity with distribution-specific systems and standards (e.g., Manhattan, Blue Yonder, SAP EWM, and DSCSA track-and-trace serialization data).

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex).

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As a Senior Value Engineer specializing in the Energy Sector, you are pushing the envelope in solving business-critical problems for our strategic customers across utilities, oil & gas, and renewable energy. You will be working with our most strategic energy clients, understanding their unique objectives—from grid modernization and asset lifecycle management to navigating the energy transition—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI, and Databricks.
  
  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands our customers’ complex business realities (bridging IT and OT systems) and enables them to industrialize AI, unlocking real ROI on deployments at scale. You will prototype these solutions, demonstrate their value to Energy Executives, and ensure successful implementation, adoption, and value realization in order to increase the footprint of Celonis across the global energy landscape.

Key Responsibilities:

• AI Discovery & Solutioning: Understand energy customers' AI strategy and sector-specific challenges (e.g., predictive maintenance, outage management, supply chain resilience, or regulatory compliance). As a Celonis product and energy domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex operational problems. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points specific to power generation, transmission, and O&G operations.

• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent field service routing or autonomous procurement).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and stringent energy compliance frameworks.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the Energy vertical, scaling knowledge across the organization regarding utilities, upstream/downstream oil & gas, and renewable energy processes.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Energy sector (Utilities, Oil & Gas, and/or Renewables). This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.

• Deep understanding of business processes native to the Energy sector (such as Asset Management, Field Services, Supply Chain, Capital Projects, or Trading) with the ability to translate high-level business needs into specific, impactful AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., automated processing of engineering documents, regulatory filings, or intelligent diagnostic chatbots).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies relevant to handling large-scale industrial data.

• Strong presentation skills to both internal and external stakeholders (including C-level executives and operational leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly regulated industries.

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex) and familiarity with IT/OT convergence and industrial IoT data structures.

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As an Senior Value Engineer specializing in the Energy Sector, you are pushing the envelope in solving business-critical problems for our strategic customers across utilities, oil & gas, and renewable energy. You will be working with our most strategic energy clients, understanding their unique objectives—from grid modernization and asset lifecycle management to navigating the energy transition—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI, and Databricks.
  
  With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands our customers’ complex business realities (bridging IT and OT systems) and enables them to industrialize AI, unlocking real ROI on deployments at scale. You will prototype these solutions, demonstrate their value to Energy Executives, and ensure successful implementation, adoption, and value realization in order to increase the footprint of Celonis across the global energy landscape.

Key Responsibilities:

• AI Discovery & Solutioning: Understand energy customers' AI strategy and sector-specific challenges (e.g., predictive maintenance, outage management, supply chain resilience, or regulatory compliance). As a Celonis product and energy domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle. Lead technical discovery and capability demonstrations during the pre-sales cycle, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully reached.

• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex operational problems. Leverage cutting-edge AI technologies to rapidly build creative prototypes in customer hackathons, solving critical pain points specific to power generation, transmission, and O&G operations.

• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale, enabling a fundamental shift from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent field service routing or autonomous procurement).

• Proof Projects: End-to-end execution of business-critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with enterprise data, identity protocols, and stringent energy compliance frameworks.

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the Energy vertical, scaling knowledge across the organization regarding utilities, upstream/downstream oil & gas, and renewable energy processes.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Energy sector (Utilities, Oil & Gas, and/or Renewables). This includes defining AI roadmaps, building compelling ROI/TCO business cases, and guiding technical implementations through to value realization.

• Deep understanding of business processes native to the Energy sector (such as Asset Management, Field Services, Supply Chain, Capital Projects, or Trading) with the ability to translate high-level business needs into specific, impactful AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., automated processing of engineering documents, regulatory filings, or intelligent diagnostic chatbots).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies relevant to handling large-scale industrial data.

• Strong presentation skills to both internal and external stakeholders (including C-level executives and operational leaders), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly regulated industries.

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex) and familiarity with IT/OT convergence and industrial IoT data structures.

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Lantern is looking for a Director of AI Engineering to lead the strategy, development, and delivery of AI and machine learning capabilities across the organization. This is a senior leadership role responsible for building and scaling a high-performing AI engineering team, setting the technical vision for ML/LLM systems, and ensuring that AI initiatives are tightly aligned with Lantern’s product strategy, care workflows, and business objectives. You will report to the VP of Engineering and work closely with executive leadership to shape how AI transforms care delivery and operations at Lantern.

In this role, you will own the end-to-end AI engineering function—from research and prototyping through production deployment and continuous improvement. You will be expected to move fast, driving rapid proof-of-concept development and delivering production-grade AI solutions on aggressive timelines. You will establish the technical roadmap, define engineering standards, build team culture, and drive execution across multiple concurrent AI initiatives. Your work will directly impact how Lantern leverages intelligent systems to improve patient outcomes, operational efficiency, and scalable growth.

Location: Hybrid - at least 3 days/wk in one of our offices:  Dallas, TX / Chicago/Evanston / New York / Washington, DC

Responsibilities:

• Define and execute the AI engineering strategy and technical roadmap, ensuring alignment with Lantern’s product, clinical, and business priorities.
• Build, lead, and scale a high-performing AI engineering team, including hiring, mentoring, performance management, and creating a culture of technical excellence and innovation.
• Drive rapid AI proof-of-concept development, moving from problem identification to working prototypes on aggressive timelines, and shepherding successful POCs into production-grade systems.
• Own the end-to-end lifecycle of ML/LLM systems—from research and experimentation through production deployment, monitoring, and continuous improvement.
• Establish and maintain best-in-class MLOps practices: experiment tracking, reproducibility, CI/CD for models, automated testing, drift detection, observability, and scalable infrastructure on Azure.
• Partner with product, clinical operations, marketing, data, and engineering leadership to identify high-impact AI opportunities and translate them into well-scoped, executable initiatives.
• Lead architecture decisions for ML/LLM systems, ensuring scalability, reliability, security, and compliance with healthcare regulations.
• Establish clear KPIs and success metrics for the AI engineering function, including model performance, delivery velocity, team health, and business impact.
• Champion responsible AI practices across the organization, including fairness, transparency, explainability, and regulatory compliance in healthcare contexts.
• Represent the AI engineering function to executive leadership, providing regular updates on progress, risks, and strategic recommendations.
• Build external visibility for Lantern’s AI capabilities through thought leadership, community engagement, and talent brand building.

Experience & Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience; Master’s preferred.
• 10+ years of experience in software engineering or machine learning, with at least 4 years in a leadership role managing ML/AI teams.
• Proven track record of building and shipping production ML/LLM systems at scale, including end-to-end ownership from research through deployment.
• Deep technical expertise in ML frameworks (PyTorch, TensorFlow, scikit-learn), LLM technologies (prompt engineering, RAG, embeddings, fine-tuning, agents), and modern MLOps practices.
• Strong experience with cloud-native ML infrastructure on Azure, including Azure ML, Databricks, and Azure DevOps pipelines.
• Demonstrated ability to rapidly prototype AI solutions and drive them to production on aggressive timelines.
• Experience with big data ecosystems (Spark, Databricks, Delta Lake) and streaming technologies.
• Excellent leadership skills: hiring and developing high-performing teams, managing cross-functional relationships, and influencing at the executive level.
• Strong strategic thinking with the ability to balance long-term vision with near-term execution and business impact.
• Experience operating in regulated environments, ideally healthcare, with an understanding of compliance, data governance, and responsible AI principles.
• Outstanding communication skills with the ability to translate complex technical concepts for non-technical stakeholders and executive audiences.

Strong Candidates will have:

• Experience applying AI/ML capabilities in healthcare, health tech, or other regulated industries.
• Background working with clinical data, claims data, or healthcare operational workflows.
• Experience building and scaling AI teams from the ground up, including defining team structure, processes, and culture.
• Track record of delivering AI-powered products that drove measurable business outcomes.
• Experience with LLM agents, agentic workflows, tool-use orchestration, or multi-step reasoning systems.

Benefits

• Medical Insurance
• Dental Insurance
• Vision Insurance
• Short & Long Term Disability
• Life Insurance
• 401k with company match
• Paid Time Off
• Paid Parental Leave

Who We Are

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials.  Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

Where You Fit 

As the Associate Director, MLOps Lead, you will lead the team responsible for the backbone of our AI/ML Stack: the infrastructure that bridges ML research and massive-scale production. Your primary directive is to evolve our stack to meet the next scale of needs in large scale ML training & inference workloads.  

You’re someone who enjoys designing and building for reliability, relishes collaboration and technical challenges, and takes pride in making things better – without taking yourself too seriously. Our technical space is broad: high-scale AI training & inference workloads, cloud infrastructure, Kubernetes, observability, distributed systems, and a bit of everything in between.

What You’ll Do

This role is critical for driving the scalability and efficiency of our Machine Learning Operations platform with high-impact & high growth strategic initiatives. 

• Vision and Roadmap: Develop and execute the long term vision & roadmap for MLOPs team to support ML development and deployment needs across the business units. Successfully manage the tension between short-term tactical deliveries and long-term architectural transformation for future growth. 
• Team Management: Lead and mentor a team of 6-7+ high-performing engineers. Strategically allocate resources to manage support for existing services while executing key strategic initiatives.
• Cross-Functional Collaboration: Partner with leaders across machine learning, data science, product engineering, and infrastructure to proactively identify pain points, address bottlenecks, and facilitate the deployment of new solutions.
• Foundation Model Readiness: Architect the compute and storage pipelines required for ML Engineers to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency.
• Inference Modernization: Modernize the AI Product inference stack to support 5-10x growth of AI runs across global deployments.
• System Observability: Collaborate with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute under-utilization, network bottlenecks, and granular cost and turn-around-time attribution.
• Technology Refresh: Conduct "Build vs. Buy" assessments, leading "Stack Refresh" audits to benchmark our proprietary tools against best-in-class commercial and open-source alternatives to meet our future needs.

What You Bring

To be successful in this role with us, you'll at least need:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
• 2-3+ years of experience managing engineering team(s), with a focus on building production-grade frameworks for MLOps or ML Infrastructure.
• Deep technical expertise with ML workloads on kubernetes, cloud computing platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow, or proprietary equivalents) and DevOps principles and infrastructure-as-code (Helm, Terraform).
• Proven experience managing petabyte-scale datasets and high-throughput production inference pipelines.
• Strong software engineering skills in complex, multi-language systems and experience with scalable service architecture.
• Use of AI assistants (e.g. CoPilot, Cursor, Claude) across platform development lifecycle.

It Would Be Great If You Also Have

• Exposure to ML frameworks like PyTorch or Scikit-learn.
• Experience with large-scale data processing frameworks (e.g. Spark, Hive, Databricks, Amazon EMR)
• Expertise in MLOps principles, including model lifecycle management, feature stores, model monitoring, and CI/CD for ML.
• Familiarity with security and compliance best practices in ML systems.

We Want To Hear From You

At PathAI, we are looking for individuals who are team players, are willing to do the work no matter how big or small it may be, and who are passionate about everything they do. If this sounds like you, even if you may not match the job description to a tee, we encourage you to apply. You could be exactly what we're looking for. 

PathAI is an equal opportunity employer, dedicated to creating a workplace that is free of harassment and discrimination. We base our employment decisions on business needs, job requirements, and qualifications — that's all. We do not discriminate based on race, gender, religion, health, personal beliefs, age, family or parental status, or any other status. We don't tolerate any kind of discrimination or bias, and we are looking for teammates who feel the same way.

The cash compensation outlined below includes base salary or hourly wage and on-target commission for employees in eligible roles. The summary below indicates if an employee in this position is eligible for annual bonus, overtime pay and equity awards. Individual compensation packages are tailored based on skills, experience, qualifications, and other job-related factors. 

Annual Pay Range:
AD, MLOps: $181,500 - $278,300

Not Overtime Eligible

Eligible for Equity

TripleLift, a fast-growing startup tackling some of the most challenging problems in the world of digital advertising, is seeking a Data Scientist with a focus on optimization strategies for our real-time ad marketplace. As a Data Scientist at TripleLift, you will contribute to experiments to understand bidding behaviors in our real-time marketplace, improving the performance of traffic shaping algorithms, and optimizing outcomes for Publishers, DSPs and TripleLift alike. You will build proof of concept models, put ML models in production, create reusable features and data structures, and collaborate with cross-functional teams to drive Data Science initiatives forward. 

Responsibilities 

• Conduct research and experiments to improve the performance of our traffic shaping algorithms and maximize performance for our customers
• Build new traffic shaping optimization strategies based on cutting edge research, build proof of concept ML models, and drive them to successful outcomes in full scale production
• Support analytics projects in partnership with Product, Engineering, and cross-functional teams to support and influence product strategies
• Monitor and measure statistical modeling performance, and build dashboards and alerts to ensure models are functioning effectively
• Build reusable modules and data structures, and provide guidance and feedback to team members on their work, taking into account their skills, backgrounds and working styles 
• Take ownership of complex technical challenges, demonstrating initiative in navigating ambiguity and championing engineering best practices and innovation.

Qualifications 

• Bachelor’s degree or higher in a related quantitative field (E.g. Mathematics, Computer Science, Engineering)
• 2+ years of work experience in Data Science / Machine Learning
• Familiarity with tools used in our tech stack is a plus (e.g. Python, Spark, AWS, Docker, DataBricks, ONNX, MySQL, Snowflake and Airflow)
• Familiarity with ML libraries like scikit-learn to quickly analyze data and prototype models that can be used in high volume distributed systems
• Familiarity with monitoring and measuring statistical modeling performance via dashboards and alerts, using tools like Prometheus, Grafana, Looker, etc., to make sure models are functioning effectively
• Solid software engineering fundamentals, including version control, testing, and writing clean, maintainable, and production-ready code
• Excellent technical communication skills 
• Strong analytical and problem-solving skills
• Committed to a process of continuous learning and spreading subject matter expertise

Technologies

From our early days, we’ve always believed in using the right tools for the right job and continue to explore new technology options as we grow. The Data Science team uses the following technologies at TripleLift:

• Languages: Python, Java
• Frameworks: Spark, DataBricks, ONNX, Docker, Airflow
• Databases: MySQL, Snowflake, S3/Parquet
• Amazon Web Services to keep everything running

About Sovrn

Every interesting company solves important problems for other people.  Sovrn is a Software and Data business that helps Open Web businesses be and remain independent.  We help them understand their business better, operate more efficiently, and make & keep more money.

• We believe in the freedom and free-flow of information.
• We believe the Open Web is the largest source of this information.
• We believe in helping Open Web businesses be and remain Independent.

Through Software products and Data solutions we help our customers:

• Understand their business better, so they can make better decisions
• Operate their business more efficiently, so they can invest in what matters most
• Make (and Keep) more money, so they control their own destiny

About the Role

The Senior AI/ML Data Scientist at Sovrn is a strategic technical leader who drives high-impact data initiatives across the organization. This role requires a unique blend of strategic vision ("the art of the possible") and tactical execution ("think and execute tactically"). You will solve ambiguous problems to deliver measurable business value and influence the future of data science in AdTech. You will guide architecture, experimentation, and product design while ensuring scientific and technical rigor. This position requires technical excellence, business acumen, and leadership.

What you’ll be doing:

• Operate at the frontier of applied AI as a hands-on practitioner — personally exploring, prototyping, evaluating, and deploying cutting-edge Agentic AI technologies to bring new, grounded insight into product, modeling, and optimization decisions. 
• Design, implement, and deploy advanced predictive models, algorithms, and experiments that drive core business outcomes, specifically optimizing ad performance, user engagement, and monetization strategies.
• Analyze large-scale datasets to identify patterns, trends, and optimization opportunities, while maintaining and improving data pipelines and experimentation frameworks to support ongoing research.
• Define and evangelize best practices in modeling, testing, measurement, and data governance, ensuring scientific rigor while balancing practical business constraints and timelines.
• Collaborate cross-functionally to identify high-leverage AI/ML opportunities, translate them into actionable roadmaps, and deliver measurable outcomes.
• Mentor other data scientists through code review, experiment design, and technical feedback and model what it looks like to stay current with fast-moving AI research in a production-focused environment. 
• Translate complex data problems into strategic decisions through compelling storytelling and clear communication of findings and recommendations to both technical and non-technical stakeholders.

A successful candidate will have:

• Proven ability to deploy autonomous agents to production including optimizing inference performance, managing latency/cost tradeoffs, and ensuring robust model evaluation at each stage.
• Demonstrates prompt engineering discipline by systematically evaluating prompts across model families using quantitative evaluation.
• Active use of AI tools in daily technical work — integrates coding assistants, orchestration frameworks, and search-augmented workflows in ways that accelerate prototyping and reduce iteration cycles.
• Experience in data science and applied machine learning, with a strong record of delivering business impact.
• Deep technical proficiency across classical ML (supervised/unsupervised), reinforcement learning, and deep learning, with a strong foundation in statistical modeling, A/B testing, and causal inference.
• Proficiency with Python, SQL, cloud-based data ecosystems, modern AI/ML frameworks (e.g., Pandas, Scikit-Learn, PyTorch/TensorFlow, LangChain, Hugging Face, PydanticAI), experience with large-scale datasets and distributed computing frameworks (e.g., Spark, PySpark), and Databricks running on AWS Cloud.
• Strong understanding of ad tech ecosystems (e.g., RTB, DSP/SSP workflows, auctions, and yield optimization) is a big plus.
• Demonstrated ability to influence across technical and non-technical audiences, lead cross-functional initiatives, and work in a collaborative, agile environment (Scrum and Kanban).
• A strategic mindset with strong product intuition and experience working at scale.

Location: This position is based in our Boulder, Colorado office. For exceptional candidates we would consider remote locations. Applicants need to be able to commute/travel to our Boulder location.

We understand that no candidate is perfectly qualified for any job. Experience comes in different forms; many skills are transferable; and passion goes a long way. Even more important than your resume is a clear demonstration of accountability and the ability to thrive in a fluid and collaborative environment. We expect you to learn new things in this role and encourage you to apply if your experience is close to what we're looking for.

Sovrn Core Values: Candid, Customer Empathy, Learning, Scrappy, Second Order Thinking

Compensation and Benefits

The base salary for this position in Boulder, Colorado is $165,000 to $195,000 annually. In addition to salary, the total compensation package includes bonus, equity, and benefits. Actual earnings may vary depending on the candidate's direct experience, skills, knowledge, and location.

Sovrn offers a full slate of benefits from competitive salaries, stock options, medical, dental, and vision coverage, short and long-term disability, life insurance, 11 paid holidays, flexible vacation, commuter benefits, a 401(k) plan and match, and a paid parental leave program.

Job Posting

Applications will be accepted until May 30, 2026.

Equal Opportunity Employer 

Sovrn is proud to be an Equal Opportunity Employer and provides equal employment opportunities to all employees and applicants regardless of race, color, religion, gender, gender identity, age, national origin, disability, parental or pregnancy status, marriage and civil partnership, sexual orientation, veteran status, or any other characteristic protected by law. Reasonable accommodations will be made to meet the requirements of the Americans with Disabilities Act and will be provided as requested by candidates taking part in all aspects of the selection process. 

Recruitment Agencies

Sovrn does not accept agency resumes. Please do not forward resumes to our jobs alias or Sovrn employees. Sovrn is not responsible for any fees related to unsolicited resumes.

About Snorkel

We’re on a mission to democratize AI by building the definitive AI data development platform. The AI landscape has gone through incredible change between 2016, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

As an Applied AI Engineer (Pre-Sales), you will partner closely with Sales and our AI Solution Practice Leaders during pre-sales engagements. You will lead technical discovery and solution scoping to identify high-value use cases, and develop tailored demos and pilots that demonstrate how modern GenAI and machine learning techniques can drive measurable business outcomes. You will also translate proposed solutions into clearly scoped, technically sound proposals and Statement of Works (SOWs) that set realistic expectations and position engagements for success, while collaborating with delivery teams during initial implementation.

This role sits at the intersection of deep technical problem-solving and customer partnership. You will help customers unpack complex business challenges, quickly prototype solutions, and clearly communicate technical approaches to both engineering teams and senior business stakeholders. From early architecture discussions to demo development, you will play a key role in helping organizations adopt AI effectively while surfacing field insights that inform our product and research roadmap.

Main Responsibilities

• Partner closely with Sales and our AI Solution Practice Leaders to shape technical strategy across active prospects, win technical evaluations, and ensure a seamless transition to post-sales delivery through structured handoff documentation and clear expectation alignment.
• Lead structured technical discovery engagements with prospects to understand business objectives, success criteria, data landscape, architectural constraints, security considerations, and organizational readiness.
• Translate discovery findings into well-defined GenAI solution architectures that demonstrate technical feasibility and business impact using internal frameworks, and complementary third-party technologies.
• Design, build and deliver bespoke demos, including evaluation pipelines, dataset strategy, retrieval-augmented generation systems, fine-tuning workflows, prompt engineering strategies, and agentic architectures tailored to specific customer use cases and senior business stakeholders.
• Author and contribute to custom proposals, including Statements of Work and RFP/RFI responses, ensuring scope clarity, architectural soundness, realistic effort estimates, and alignment with delivery capabilities.
• Systematically capture reusable patterns from bespoke demos and evolve them into reference architectures, demo assets, benchmarks, and internal solution playbooks.
• Annual travel up to 25%.

Preferred Qualifications

• B.S. in Computer Science, Engineering, Math/Statistics, or equivalent experience.
• 8+ years in customer-facing technical roles (pre-sales, solutions engineering, or applied AI), including discovery, scoping, demos, proof-of-value engagements, and RFP responses.
• Hands-on Python experience and familiarity with the modern Gen AI stack -  LLM ecosystems, RAG, vector databases, data processing, synthetic dataset curation, evaluation workflows, LLM orchestration and agent authoring tools.
• Expertise across the predictive ML stack, including classical ML (e.g., scikit-learn) and data processing frameworks (e.g., pandas, Spark).
• Ability to operate in fast-paced environments and rapidly build prototypes (ML solutions, RAG systems, prompt-based workflows, fine-tuned models, agentic systems) that demonstrate business value.
• Able to translate ambiguous business problems into testable technical approaches and measurable success criteria.
• Strong presentation and storytelling skills with the ability to engage both technical and executive audiences with credibility.
• Experience estimating scope and producing technical content for proposals and Statements of Work.

Compensation range is $190K - $300K OTE. All offers also include equity in the form of employee stock options. Our compensation ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Locations

New York; San Francisco Bay Area; Austin; Dallas; Seattle; Atlanta; Nashville; Boston; Remote - US

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