Staff GenAI Research Scientist - Retrieval

P-1214

At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform, so our customers can focus on the high value challenges that are central to their own missions.

The Databricks AI Research organization enables companies to develop AI models and systems using their own data, with technologies ranging from pre-training LLMs from scratch to augmented generation using the latest retrieval techniques. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company’s AI models are just as valuable as any other core IP, and that high-quality AI models should be available to all.

Job Description 

As a Staff Research Scientist on the Enterprise Agents team, you will be responsible for keeping up with the latest developments in Agent Context, Information Retrieval, Knowledge Assistants, RAG, Embeddings, and advancing the scientific frontier by creating new techniques that go beyond the state of the art. You will work together on a collaborative team of researchers and engineers with diverse backgrounds and technical training. And most importantly, you will love our customers: our goal is to make our customers successful in applying state-of-the-art LLMs and AI systems, and we encode our scientific expertise into our products to make that possible.

The Impact you will have

As a Staff Research Scientist on the AI Research Team at Databricks, You Will: 

• Define and lead independent research agendas on Information Retrieval for reusable Agent components, conducting experiments to empirically validate hypotheses and benchmark against state-of-the-art approaches.
• Drive research and technical strategy across enterprise retrieval and search, including workspace search, vector search.
• Drive algorithmic innovations for Retrieval within Large Language Models. 
• Experience with Agent Benchmarks for retrieval, text-embeddings and LLM Data Quality. 

What We Look for

• 5+ years of 
• PhD in Computer Science or related field with strong foundations in Natural Language Processing and Information Retrieval 
• Developing and implementing methods that extend and improve model capabilities, reliability, and safety. 
• Strong preference for candidates with first-author publications at top ML/systems conferences (EMNLP, ICLR, ACL, NeurIPs) focused on optimization or efficiency.

What You’ll Be Part Of:

An iSpot Research Data Scientist is a key contributor to the future growth of the company, pushing boundaries on what is measurable in the TV viewing and advertising space. The Research Data Science team builds innovative solutions for iSpot’s audience measures, attribution and lift analytics, and creative testing. After developing new methodologies and building prototypes, we work with our product and engineering teams to scale our models to satisfy the needs of brands, publishers, networks, and agencies in a constantly evolving marketing landscape.

Responsibilities:

Qualifications and Education Requirements:

Preferred Skills:

Target cash compensation range: $95,950 - $106,000 USD Annually

We are committed to providing competitive, market-informed compensation. The cash compensation above includes base salary, variable commission for employees in eligible roles, and annual bonus targets for eligible roles. In addition to cash compensation, all full time iSpotters are eligible to participate in iSpot’s equity plan to receive stock options. Non-exempt roles will also be eligible for (pre-approved) overtime pay. Individual compensation packages are influenced by different factors unique to each candidate, including their skills, experience, qualifications and other job-related reasons.

For more information on total rewards package, go HERE

Hybrid & Flexible Workplace Policy

iSpot supports a hybrid and flexible workplace. Depending on location and work responsibilities, employees may be designated as full-time or part-time office-based or a fully remote employee. A hybrid work schedule indicates that you work in the office some days and work from home other days. The best hybrid workplaces allow for flexibility while also encouraging consistency. 

Those local or living in surrounding areas to one of our offices (Bellevue, WA or New York, NY) will work a hybrid schedule, coming into their local office 1-3 days a week. While those in a role, not office-based and located further away from our offices, will work a fully remote schedule. If you have questions regarding exact details of our hybrid & flexible workplace policy, please let your recruiter know and they will discuss with you further.

#LI-Remote

Hex's user base is continuing to grow rapidly. We are hiring a Sales Engineer to partner with potential customers to support their Hex evaluations.

About the role

Hex is redefining how teams work with data - combining notebooks, apps, BI, and AI-native workflows into a single collaborative analytics platform. Companies like Anthropic, Cursor, and Brex rely on Hex as a core part of how their data teams operate.

We’re at an exciting stage of growth. Hex is scaling rapidly, with strong momentum in the mid-market and SMB segments. This is a high-impact moment to join: our go-to-market motion is accelerating, our AI capabilities are evolving quickly, and Sales Engineers play a central role in shaping how we win.

As a Sales Engineer, you will leverage your product, ecosystem, and technical expertise to shape how our potential customers evaluate and adopt Hex. You will partner with the Sales team to help prospects understand the challenges in their current state, connect Hex’s value to those challenges, own the technical evaluation, and set them up for long-term success as a customer. This is a consultative, highly technical position where you’ll influence deal strategy, inform product direction, and act as the voice of the customer.

If you want to learn more about the team, what we do, and how we work, please check out our team page!

What you will do

• Serve as a trusted technical advisor to data teams (think: Data Scientist or Analyst writing SQL and Python) and build strong relationships to help them get the most out of Hex during their evaluations
• Own the technical sales cycle and work with end users throughout the Sales cycle from discovery and product demos through trials, and proofs of concept
• Partner closely with Account Executives to develop opportunity-level strategy, execute evaluations, and share feedback to improve your partnership
• Inform the product roadmap by synthesizing product feedback from prospects to share with internal Engineering & Product teams
• Project manage the architecture, security, and technical reviews that prospects require
• Develop content to support users who are a part of evaluations ie. demo projects, documentation, videos, etc.
• As needed, travel to customer on-sites and marketing events to deliver demos and build strong customer relationships

Who you might be

You might be a fit for this role if you have:

• 5+ years of professional work experience
• 3+ years in a Sales Engineer, Solutions Engineer, or similar customer-facing technical role
• Experience working with Mid Market customers (250-2500 employees)
• Experience with land POC/Technical Sales Motions
• Familiarity with data science tooling and workflows, including Python, SQL, and BI tools (Bonus points if you have spent lots of time in notebooks, SQL IDEs, or app-building frameworks.)
• A strong track record of talking to customers - from IC users to senior stakeholders - and solving their problems
• You're an excellent communicator who builds empathy, shares feedback, and grows successful relationships with internal and external stakeholders
• A generalist technical skillset: you don't need a CS degree, but should have a passion and interest for technology and be a creative problem solver
• Experience or interest in working in an early-stage startup environment

In addition to our unique culture, Hex proudly offers a competitive total rewards package, including but not limited to, market-benched salary & equity, comprehensive health benefits, and flexible paid time off.

The OTE (base + variable) range for this role is: $175,000 - $200,000.

The salary range shown may be a reflection of additional factors such as geographical location and skill ranges/levels we’re open to. Placement in the salary range will be decided upon completion of the interview process, taking into account factors like leaving room for growth, internal fairness & parity, your demonstrated skills, and the depth of your experience. Our Recruiting team will be able to provide more details during the process.

By submitting an application the candidate consents to the use of their personal information in accordance with the Hex Privacy policy: https://learn.hex.tech/docs/trust/privacy-policy.

About the role

As part of our growing Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You’ve worked in cultures of excellence in the past, and are eager to apply that experience to help shape the cultural norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world.

Responsibilities:

• Define core metrics, build measurement frameworks, and maintain core reporting to evaluate success
• Deep dive into product and user data to derive actionable insights and size opportunities to improve products, strategy and operations, influencing roadmaps through your insights and recommendations
• Develop hypotheses, apply rigorous causal inference methods – controlled experiments, synthetic controls –  and analyze the results in order make actionable recommendations 
• Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions
• Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes
• Present complex technical analyses and recommendations to both technical and non-technical stakeholders
• Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration and decision-making as our products grow

You may be a good fit if you have:

• 7+ years of experience in data science or analytics roles 
• Deep expertise with Python, SQL, and data visualization tools
• Expertise with experimental design, causal inference, statistical modeling, and A/B testing frameworks, particularly in high-scale technical environments
• Highly effective written communication and presentation skills
• A track record of translating complex data into clear, actionable insights for both technical and business stakeholders
• A bias for action and ability to thrive in ambiguous, fast-moving environments where you must create clarity and drive forward progress
• A passion for the company’s mission of building helpful, honest, and harmless AI
• Some experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem

We’re hiring across multiple pillars

Applying for this role will allow you to be considered for all pillars currently hiring. You will be asked to select a preference when submitting an application.

Claude Code

This role is embedded with the Claude Code product team, driving data-informed decisions for Anthropic's agentic coding tool that enables developers to delegate coding tasks directly to Claude from their terminal. You'll help the team understand how developers interact with AI coding assistants, measure developer productivity and product quality, and identify opportunities to innovate the developer experience as the product scales. Key focus areas include developer usage patterns across the platform, driving adoption within the developer ecosystem, and developer segmentation.

Strong candidates may have:

• Deep familiarity with software development workflows, developer tools, and engineering productivity metrics—ideally from working at developer-focused companies or on products targeting software engineers
• Experience analyzing human-AI interaction patterns, particularly in code generation or developer tooling contexts
• Experience supporting product teams building for the Enterprise

Consumer Data Scientist

You will be embedded with our Consumer product team. This team is responsible for building all consumer-facing Claude experiences—including web, mobile, desktop, and browser extensions. In this role, you'll shape how millions of users interact with Claude daily, driving product insights to product recommendations for interfaces that are intuitive, responsive, and push the boundaries of what AI-powered applications can be.

Strong candidates may have:

• Experience working closely with Product and Engineering teams on consumer products across multiple platforms (web, iOS, Android, desktop, browser extensions)
• Demonstrated impact on product roadmap and strategy from data deep dives in consumer growth, engagement and retention
• Expert in experimentation, holding a high statistical bar for measuring the impact of core product changes

Platform Product Data Scientist

You will partner closely with product, engineering, and go-to-market teams to understand how developers and enterprise customers build on and adopt the Claude Developer Platform—spanning our core API, agent orchestration, tool and MCP integrations, and knowledge management capabilities. You'll identify growth opportunities, surface insights about how AI agents are being built and deployed at scale, and drive data-informed decisions that shape our platform roadmap.

Strong candidates may have:

• 3+ years of experience working closely with Product or Engineering teams on API or developer-facing products, with demonstrated impact on product roadmap and strategy
• Experience supporting B2B sales teams with data insights
• Strong instincts for what drives product adoption, engagement, and retention

Model Experience Data Scientist

You will work closely with product, engineering, and research leaders to bring data-driven rigor to every phase of model development and launch. Sitting at the true bleeding edge of putting frontier AI research into the public domain, you will leverage data from both external customers and internal testing to define and measure key company success metrics, and analyze user and model behavior to identify new opportunities to push the frontier. 

Strong candidates may have:

• 3+ years of experience deeply embedding in Product or Research teams, preferably working with LLM or AI products
• Comfort working with unstructured data

About the role

Anthropic is compute-constrained, and how we allocate that compute is one of the highest-leverage decisions we make as a company. Today, those allocation choices are only loosely tied to the user outcomes we ultimately care about — retention, lifetime value, and the experience of people relying on Claude. You will change that.

As a hands-on technical IC on the Supply pillar of our Data Science & Analytics team, you'll sit alongside the infrastructure engineers who run our compute and help decide how our scarcest resource gets used. You'll design and run the analyses, observational and synthetic experiments, and optimization frameworks that turn opaque supply decisions into shared, measurable understanding across the company. Your work will directly shape how frontier AI reaches the world at scale, and your findings will go in front of senior leadership, including our CTO and his staff.

This role is a fit for someone who thinks natively in terms of constrained allocation and queueing, who enjoys getting close to the system rather than analyzing it from a distance, and who wants their analyses to translate into operational changes that ship.

Responsibilities:

• Build and run testing frameworks — observational and synthetic — to quantify how different inputs affect compute allocation outcomes
• Connect compute allocation decisions to downstream user outcomes (retention, LTV, revenue) so we stop optimizing in a vacuum
• Partner closely with infrastructure engineers, product, and research to instrument the system, measure what matters, and ship operational changes
• Develop the metric hierarchies, dashboards, and reporting that turn supply decisions into shared understanding across the company
• Contribute analyses and recommendations to executive forums, and co-author the supply narrative the team takes to the CTO and his staff
• Measure and improve how AI affects developer productivity inside Anthropic

You may be a good fit if you have:

• Strong technical IC background in data science, analytics, or operations research
• Operations research foundation — you think natively in terms of optimization, constrained allocation, and queueing
• Deep proficiency with Python, SQL, and data visualization tools
• Track record of owning analyses end-to-end and communicating results clearly to engineering and product leadership
• Direct experience working closely with engineering teams on production systems
• A passion for Anthropic's mission of building helpful, honest, and harmless AI

Strong candidates may have:

• 5+ years of technical IC experience in data science, analytics, or operations research; 8+ years for candidates targeting a Staff-level scope
• Experience with highly complex systems with many interacting components (ad networks, payment processing, marketplace matching, routing, etc.)
• Experience with causal inference methods applied to operational decisions (synthetic controls, geo-experiments, switchbacks)
• Experience contributing to or designing experimentation platforms, not just using them
• Exposure to AI/ML products, large language models, or large-scale inference systems
• Track record of setting technical direction across multiple workstreams or mentoring senior ICs without formal management responsibility

Deadline to apply: None. Applications are accepted on a rolling basis.

Hi, we're Oscar. We're hiring an Applied AI Engineer to join our AI team.

Oscar is the first health insurance company built around a full stack technology platform and a focus on serving our members. We started Oscar in 2012 to create the kind of health insurance company we would want for ourselves—one that behaves like a doctor in the family.

About the role

Join Oscar’s AI team to build and launch transformative AI-powered products that redefine the healthcare experience for millions. As an Applied AI Engineer, you’ll take end-to-end ownership of high-impact projects—conceiving, prototyping, and deploying generative AI solutions that solve real-world problems across the healthcare ecosystem.You’ll work alongside a world-class team of engineers and data scientists, rapidly iterating on ideas and shipping production-ready applications that drive measurable business and user outcomes. You’ll have the autonomy to identify opportunities, experiment with the latest AI models and techniques, and deliver solutions that streamline operations, improve patient experiences, and move the needle for Oscar and our members.

You will report to the Sr. Director, AI.

Work Location: This position is based in our New York City office, requiring a hybrid work schedule with 3 days of in-office work per week. Thursdays are a required in-office day for team meetings and events, while your other two office days are flexible to suit your schedule. #LI-Hybrid

Pay Transparency: The base pay for this role is: $180,504 - $236,911 per year. You are also eligible for employee benefits, participation in Oscar’s unlimited vacation program, company equity grants, and annual performance bonuses.

Responsibilities

• Lead the design, development, and deployment of AI-driven products and features, from zero-to-one prototypes to robust production systems.
• Collaborate cross-functionally with product, design, and business teams to deeply understand user needs and deliver solutions that move key metrics.
• Rapidly experiment with new models, techniques, and tools (including LLMs, RAG, and agentic workflows), iterating based on data and user feedback.
• Build and scale full-stack applications and intelligent agents that automate complex healthcare workflows and deliver seamless user experiences.
• Stay on the cutting edge of the AI ecosystem, evaluating and integrating emerging technologies to keep Oscar at the forefront of healthcare innovation.
• Contribute to Oscar’s AI strategy and technical vision, helping shape the future of healthcare through technology.
• Other duties as assigned

Qualifications

• 7+ years of professional experience in software engineering, data science, or a related field, with a track record of impactful accomplishments
• Strong Python programming skills
• Experience solving complex, ambiguous problems 
• Experience designing and analyzing experiments or evaluations
• Experience or an interest in prototyping full-stack applications

Bonus Points

• Experience with the healthcare industry. Familiarity with claims or clinical data.
• Degree in math, statistics, data science, or engineering.
• Our ideal candidate is a data scientist with an engineering background, an engineer with a data science background, or a founder-type who combines product thinking, rapid prototyping, and hands-on coding to deliver real-world impact.
• Experience with NLP, neural networks, transformers, and modern AI frameworks.
• Knowledge of containerization and deployment tools (e.g., Docker, Kubernetes) or full-stack development (e.g., React, Golang).
• Experimental mindset: comfort with testing hypotheses and iterating on failures.

The mission of MongoDB Research is to advance the state of the art in database systems by conducting fundamental and applied research that directly impacts MongoDB's products. Our goal is to foster innovation that benefits MongoDB by addressing critical challenges in data management, performance optimization, security, scalability, and usability. Through collaboration, publication, and active engagement with leading research institutions, we strive to shape the future of database technology and ensure MongoDB remains at the forefront of innovation.

We seek an exceptional researcher specializing in database systems with particular expertise in database architecture, including query optimization, storage engines, indexing, and multicore processing. The ideal candidate will demonstrate a strong track record in designing and implementing highly performant database systems.

This role can be in-office, flexible, or based remotely in North America.

Responsibilities:

• Conduct innovative research in query optimization, indexing methods, storage engines, and performance optimization
• Design, prototype, and empirically evaluate novel database systems and architectures that leverage modern hardware features, such as SIMD parallelism, GPU acceleration, and multicore processing
• Collaborate closely with our Engineering teams to translate cutting-edge research into practical solutions applicable in commercial database products
• Publish influential research outcomes in top-tier database and systems conferences and journals (e.g., VLDB, SIGMOD)
• Build a Database Systems research group with a critical mass of 2-3 researchers over the next 1-2 years
• Mentor junior researchers, fostering a collaborative and productive research environment

Qualifications:

• Ph.D. in Computer Science, Computer Engineering, or a closely related field with a specialization in database systems
• 12+ years of experience applying computer science concepts to large scale software development problems, including databases and distributed systems
• Proven track record of impactful research publications in top-tier database and systems conferences and journals
• Demonstrated expertise in query optimization, efficient data structures, and high-performance database architectures
• Strong background in performance engineering, hardware-aware optimizations, and database engine internals
• Experience in building research prototypes and conducting rigorous empirical studies to validate theoretical models
• Excellent analytical, communication, and interpersonal skills, with the ability to collaborate across teams

Success metrics:

• Within the first three months, you will have established working relationships with key stakeholders across Product and Engineering
• Within the first six months, you will have completed a review of the company’s database roadmap and identified opportunities for research impact. You will have established a clear research agenda for the team
• Within the first twelve months, you will have successfully attracted and interviewed researchers to join the team 

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform—the most widely available, globally distributed database on the market—helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and nearly 60,000 customers—including 75% of the Fortune 100 and AI-native startups—relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions 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.

Req ID: 1263126935

Verana Health, a digital health company that delivers quality drug lifecycle and medical practice insights from an exclusive real-world data network, recently secured a $150 million Series E led by Johnson & Johnson Innovation – JJDC, Inc. (JJDC) and Novo Growth, the growth-stage investment arm of Novo Holdings. 

Existing Verana Health investors GV (formerly Google Ventures), Casdin Capital, and Brook Byers also joined the round, as well as notable new investors, including the Merck Global Health Innovation Fund, THVC, and Breyer Capital.

We are driven to create quality real-world data in ophthalmology, neurology, urology, and oncology to accelerate quality insights across the drug lifecycle and within medical practices. Additionally, we are driven to advance the quality of care and quality of life for patients. DRIVE defines our internal purpose and is the galvanizing force that helps ground us in a shared corporate culture. DRIVE is: Diversity, Responsibility, Integrity, Voice-of-Customer and End-Results. Click here to read more about our culture and values.

Our headquarters are located in San Francisco and we have additional offices in Knoxville, TN and New York City with employees working remotely in AZ, CA, CO, CT, FL, GA, IL, LA, MA, MN, NC, NJ, NV, NY, OH, PA, SC, TN, TX, VA, WA, D.C. All employees are required to have permanent residency in one of these states. Candidates who are willing to relocate are also encouraged to apply. 

Candidates based in the Bay Area, New York City, and Knoxville, Tennessee will be give priority consideration. For these locations, the position will follow a hybrid work model, with 3 days per week in office and the remaining days working from home. 

*Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.*

Job Title: Quantitative Scientist

Why This Role Matters:

The Quantitative Scientist will be responsible for executing and providing oversight and guidance on the analytical, statistical and epidemiological components of Verana research projects and data quality initiatives. The primary function of this role will be to conduct, guide, and manage the analytical execution of Verana research projects . The Quantitative Scientist will collaborate cross-functionally to build research capacity and adopt quality standards across Verana's business units.  This position requires an experienced individual with knowledge of statistics, ability to develop and review code with a solid understanding of coding best practices, a strong understanding of epidemiological study designs, as well as excellent communication and interpersonal skills.  In addition, you should be able to continually unearth and present interesting trends that have clinical, research, and business importance for our customers and oncology patients at large.  You will be joining a friendly, high performing and diverse research team with backgrounds in public health, health economics, biology, and statistics.  This role will report to the Vice President, Research.

WHAT WILL YOU GAIN

• Expertise in the oncology real-world data industry, a fast-growing segment of health data
• Experience executing oncology research and analytical/statistical methods to answer key questions for patients, providers, and life science companies

WHAT TO EXPECT IN YOUR FIRST YEAR AT VERANA:

In thirty days, you will:

• Understand the full suite of Verana’s research offerings and analyses
• Understand the research team’s GitHub repo and analysis template 

In three months, you will:

• Contribute to internal and external meetings related to Verana research and analyses
• Gain expert understanding of Verana’s data model  
• Implement clinical specifications on real-world data and add to the suite of functions available for analyses

In six months, you will:

• Personally execute analytical research projects
• Contribute to the development of project collateral, research protocols, abstracts, posters and manuscripts
• Guide and mentor data scientists and analysts 

In one year, you will:

• Lead the analytical execution of Verana’s research projects for life science, provider, and regulatory partners 
• Scope projects to determine feasibility, estimated costs, and expected timelines
• Provide guidance to Verana’s client partners in the use of Verana’s data model and analytical/statistical methodologies 

HOW YOU WILL IMPACT VERANA

• Lead the design and development of Verana research studies from a statistical and analytical perspective
• Contribute to the development of protocols and statistical analysis plans 
• Develop code and produce results as defined in protocols and statistical analysis plans
• Collaborate with physicians, statisticians, data scientists, business leaders, and others within and outside Verana to conduct research projects including observational studies, pharmacoeconomic analyses, and other relevant study types
• In partnership with the Research Team, communicate studies in progress and demonstrate results to key stakeholders through high quality abstracts, posters, and publications
• Manage and mentor a team of data analysts and data scientists, including reviewing code and analytical output
• Contribute to the evolution of Verana’s quality assurance framework, including identifying data quality issues during analysis
• Maintain current and in-depth understanding of real-world data analytical methodologies, along with the ability to lead and mentor internal team members in successfully executing them

WHAT YOU BRING TO THE TABLE

• Masters degree in computer science, statistics, applied math, econometrics, MPH, or similar quantitative field
• 3+ years of relevant research experience in the field of oncology (post-graduation)
• Experience working with real-world data sourced from electronic health records
• Experience analyzing claims data 
• Advanced knowledge of SQL, R programming, and version control (git)
• Deep understanding and experience with descriptive statistics, logistic and multivariate regression analysis, power calculations, propensity score analysis, and survival analysis
• Significant experience developing protocol documents, statistical analysis plans (SAPs), and manuscript tables and figures
• Evidences well-developed conceptual and integrative thinking with ability to access internal and external resources as needed 
• Project implementation and execution, including engagement with key stakeholders, with a high degree of autonomy
• Mentors junior analysts in HEOR and statistical analysis methodologies
• Excellent written and verbal communication skills
• Strong organizational and time management skills 
• Works well independently, as a team member, and as a team leader 
• Comfort working in a fast-paced, dynamic environment

NICE TO HAVE

• Experience implementing data science methodologies
• Understanding of data quality metrics and/or quality management systems

Benefits:

• We provide 100% health, vision, and dental coverage for employees 
• 401K Match
• Flexible vacation plans 
• $700 learning and wellness annual stipend 
• Headspace meditation app access

Final note:

You do not need to match every listed expectation to apply for this position. Here at Verana, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

Caution to Job Applicants: Be vigilant against potential scams. Verana Health will never ask for payment or personal information upfront. Verify company details, cross-check job offers, and trust your instincts. Any legitimate job offer will be received by a Verana Health email account (not via gmail, text or other means) Report suspicious activities to protect yourself and others in the job-seeking community.

#LI-REMOTE

Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.

Here’s a collection of articles about our product, mission, recent funding round, etc.

Job Description

You will work with the Layer Health ML team applying our existing ML workflow to tailor models for a specific clinical use case. You will design, build, evaluate, and iterate on these models — combining strong data science fundamentals with modern LLM techniques to deliver high-accuracy, production-ready solutions across new clinical domains.

This is a high-velocity, high-ownership role for someone who enjoys diving deep into error modes, making targeted improvements, and closing the loop between model performance and real-world impact.

This contract will require full-time hours.

What you’ll do:

• Own end-to-end development of models for specific use cases: Build and iterate using our ML/LLM-powered workflows for new clinical areas, from initial prototyping through production refinement.
• Conduct evaluation & error analysis: Own rigorous evaluation of model performance; perform deep error analysis to identify systematic failure modes and drive targeted improvements.
• Ship frequent, high-quality updates to models based on data, feedback, and observed edge cases.
• Leverage LLM application & tooling: Apply modern LLM techniques (prompting strategies, structured outputs, tool use, eval frameworks) to improve accuracy and robustness.
• Support analytics & metrics: Contribute to metrics, reporting, and internal dashboards that track model performance and downstream impact.

We look for:

• Good data science fundamentals, including knowledge of best practices for evaluating model performance and error analysis. (A proven track record of successful projects in healthcare or clinical applications is a bonus, but not required.)
• Familiarity with modern applied LLM techniques and their practical implementations, for example e.g. standard prompting techniques, LLM frameworks, structured output usage, etc. (any experience using these techniques is a bonus).
• Solid programming skills in Python.
• A strong technical communicator who thrives in a fast-paced environment.
• Proactive mindset to identify and solve problems, continuously improving our data science capabilities.
• An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team. 
• Willingness to be hybrid with our team in either our Boston or NYC office 3 days per week.

Expected compensation range for this role is $40 - $70/hour. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. 

If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo. 

Join us and help us transform healthcare with AI. Layer Health is committed to foster an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law. 

About the Role, Mission or Department Overview

The New York Times is a technology company committed to producing the world's most reliable and highest quality journalism. Our ability to do so relies on a talented team of expert technologists who help NYT learn from a tremendous abundance of data unique to this company.

The Algorithmic Targeting team (ALTA) is expanding its focus to promote subscriber growth through prescriptive machine learning (ML) on paid and owned media surfaces. We use methodologies like causal ML, inverse gap weighting and hierarchical bayesian modeling and deploy them at scale.

We are looking for a Machine Learning Scientist to join ALTA and contribute to all steps of the ML development and deployment cycle. You will lead the development of models that improve our marketing spend across external platforms and owned channels. You will report to our Director of Machine Learning.

Responsibilities:

• You will reframe business goals as ML tasks for delivering relevant insights, accurate predictions, and optimization
• You will implement and deploy machine learning research with robustness and reproducibility, with consideration of risks and trade-offs
• You will communicate complex ideas in machine learning while collaborating with colleagues in in engineering, analytics, product management, marketing, editorial, and executive leadership groups
• You will deploy models behind production APIs or batch processes, collaborate with engineering teams, and integrate into processes throughout The Times
• You will learn new technologies and ML methods, and adapt them into our workflows
• 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:

• PhD, MS + 2 years experience, or 3+ years experience in statistics, computational social science, applied mathematics, economics, or another quantitative/computational discipline
• 2+ years experience with open source machine learning or statistical analysis tools
• 2+ years coding experience in Python
• 1+ year experience in SQL and manipulating large structured or unstructured datasets for analysis
• 1+ year experience collaborating with multiple developers on a shared codebase

Preferred Qualifications:

• 1+ years of experience building statistical and causal inference models
• Familiarity with digital marketing concepts like Cost Per Order (CPO), Lifetime Value (LTV), and the marketing funnel
• Familiarity with experimental design and randomized control trials
• Familiarity with prescriptive machine learning
• Familiarity with modern methods for Marketing Mix Modeling (MMMs)

REQ-019981

About the Role

The New York Times is a technology company committed to producing the world's most reliable and highest quality journalism. Our ability to do so relies on a talented team of expert technologists who help NYT learn from a tremendous abundance of data unique to this company.

The Algorithmic Recommendations and Audience Data Science team aims to help users discover content across the Times' website, apps and emails by applying algorithms that make use of information about our readers' behavior and our editorial judgment. We also build internal tools for the newsroom to better understand story performance and coverage trends. We are a group of machine learning scientists and data analysts that partner with teams across The New York Times. We are looking for a Machine Learning Scientist to join the team and apply machine learning methods to meet this challenge. You will report to a Lead Machine Learning Scientist, and collaborate with partners across the company.

Responsibilities:

• You will reframe business and newsroom goals as machine learning tasks that deliver accurate predictions, relevant insights, and optimization

• You will adapt or develop machine learning algorithms in cases when existing algorithms are insufficient, while implementing simple approaches when appropriate

• You will implement and deploy machine learning research with robustness and reproducibility, with consideration of risks and trade-offs

• You will turn models into data products, collaborate with engineering teams, and integrate into processes throughout The Times

• You will communicate complex ideas in machine learning while collaborating with technical and non-technical colleagues in in engineering, analytics, product management, marketing, editorial, and executive leadership groups

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

Basic Qualifications:

• PhD, MS + 2 years experience, or 3+ years work experience in machine learning, statistics, computational social science, applied mathematics, or another quantitative/computational discipline

• 2+ years experience with open source machine learning or statistical analysis tools

• 2+ years coding experience in Python

• 2+ years experience in SQL and manipulating large structured or unstructured datasets for analysis

Preferred Qualifications:

• 1+ year of experience with recommendation systems, using natural language processing and small and large language models.

• 1+ years of experience with deep learning architectures, fine-tuning, embeddings and Pytorch or Tensorflow

• 1+ years of experience translating ambiguous business questions into machine learning problems

• 1+ years of experience building data products, either internal or consumer-facing

REQ-020029

Hi, we're Oscar. We're hiring a Senior Data Scientist to join our Data Team. 

Oscar is the first health insurance company built around a full stack technology platform and a relentless focus on serving our members. We started Oscar in 2012 to create the kind of health insurance company we would want for ourselves—one that behaves like a doctor in the family.

About the role:

We are a Data Science team at Oscar dedicated to improving the Member Experience by embedding advanced analytics into our core technology and operations. We focus on building data products that bridge the gap between complex health insurance workflows and seamless, member-centric service.

You will drive the evolution of our member-facing tools by developing the LLM frameworks and ML models that underpin our automated support systems and digital interfaces. You will be responsible for evaluating both tool performance and member behavior to surface deep insights that inform our broader product and operations strategy.

You will report into the Lead Data Scientist.

Work Location: This position is based in our New York City office, requiring a hybrid work schedule with 3 days of in-office work per week. Thursdays are a required in-office day for team meetings and events, while your other two office days are flexible to suit your schedule. #LI-Hybrid

Pay Transparency: The base pay for this role is: $163,944 - $215,176.50 per year. You are also eligible for employee benefits, participation in Oscar's unlimited vacation program, company equity grants and annual performance bonuses.

Responsibilities:

• Works with team and manager to impact longer term strategies and roadmap
• Sets shorter term roadmap for team
• May manage data scientists and/or analysts
• Interacts and collaborates closely with data and business counterparts across functional areas
• Researches, develops, and maintains data pipelines, statistical/ML models, and/or advanced analyses
• Compliance with all applicable laws and regulations
• Other duties as assigned 

Requirements:

• 4+ years of industry or other quantitative technical fields (which may include academia).
• 3+ years of work experience working with SQL, R, and/or Python to query, manipulate and analyze data
• 3+ years experience building data models, using more advanced analytics methods, statistical modeling, and/or data processing

Bonus points:

• Advanced degree in a quantitative or technical field
• Experience in the healthcare, finance and/or insurance industries
• Experience evaluating consumer-facing digital products in collaboration with Product and Engineering teams

The role

At HelloFresh, our mission is to change the way people eat - forever. From our 2011 founding in Europe’s vibrant tech hub Berlin, we’ve become the global market leader in the meal kit sector and inspire millions of energised home cooks across the globe every week.

Our operation planning team, located in Berlin and New York, is critical to what we do. From operation and supply chain optimization to forecasting, we work on challenging problems and have a high output of building and releasing features and engines that make our business thrive and deliver real financial impact.

As an Applied Scientist in Operations Researcher (OR), you will work together with a global, cross-functional team that is transforming supply chain management, from menu planning and procurement to production and logistics. In this role, you will play a vital part in enhancing and expanding our algorithmic planning capabilities. Your contributions will directly impact the efficiency and performance of our business operations.

What you’ll do

• Design, implement and evolve optimization models and decision-support tools for high-impact planning problems in the supply chain (e.g. assortment/menu structure, capacity, sourcing, fulfillment and network trade-offs, priorities will follow business needs).
• Drive adoption of globally developed optimization capabilities: localize assumptions, validate outputs against operational reality and coordinate staged roll-outs with stakeholders and engineering.
• Partner with engineering to productionize solvers, services and workflows; ensure ownership, runbooks, and monitoring are clear after launch.
• Build and maintain data pipelines and, where useful, dashboards so inputs, outputs, and KPIs stay transparent and we can iterate with measurable feedback.
• Spot gaps early: translate messy reality into robust, data-backed formulations and pragmatic solutions that hold up at scale.
• Collaborate across product, engineering, data, and commercial teams; represent the US planning perspective in global OR discussions so shared roadmaps stay fit for purpose.
• Champion a shared, optimization-aware, data-driven way of planning — without losing sight of simplicity and trust from business users.

What you’ll bring

• Master’s or PhD in Operations Research, Mathematics, Industrial Engineering, Management Science, or a closely related field.
• Deep knowledge of mathematical optimization and core OR methods (e.g. mixed-integer programming, decomposition and sound use of heuristics where appropriate).
• Hands-on experience with industrial solvers such as Gurobi, CPLEX, or comparable (commercial or open-source).
• Senior-level track record: Either 5+ years of relevant professional experience applying OR/optimization in industry, or a PhD plus 3+ years of post-PhD applied work, demonstrating shipped models or tools and clear ownership of outcomes.
• Strong Python and SQL for modelling, experimentation, and working with large operational datasets; familiarity with BI tools (e.g. Tableau) is a plus.
• Excellent written and verbal communication; you can explain trade-offs, build trust, and align priorities across levels.
• Autonomy and judgment: you prioritise under ambiguity, run several meaningful threads in parallel, and keep quality high as scope grows.

You’ll get…

• Competitive hourly rate, 401K company match that vests immediately upon participation, & team bonus opportunities
• Generous PTO and flexible attendance policy 
• Comprehensive health and wellness benefits with options at $0 monthly, effective first day of employment
• Up to 85% discount on subscriptions to HelloFresh meal plans (HelloFresh, Green Chef, Everyplate, and Factor_)   
• Access to Employee Resource Groups that are open to all employees, including those pertaining to BIPOC, women, veterans, parents, and LGBTQ+
• Inclusive, collaborative, and dynamic work environment within a fast-paced, mission-driven company that is disrupting the traditional food supply chain

This job description is intended to provide a general overview of the responsibilities. However, the Company reserves the right to adjust, modify, or reassign work tasks and responsibilities as needed to meet changing business needs, operational requirements, or other factors.

Octus delivers breaking news and market-moving intelligence through cutting-edge data and technology for hedge funds, investment banks, and law firms- and we’re transforming how professionals access complex and opaque information. As part of our high-performing AI Innovation team, you’ll help design, build, and productionize modern GenAI and LLM-powered systems that support both client-facing features and internal operational efficiency. You’ll work end to end—from shaping ambiguous problems into scalable solutions to deploying reliable AI models in production - collaborating closely with product, engineering, and infrastructure teams. This is a hybrid role based in NYC (3 days in office per week). Curious about what we’re building? Check out our flagship GenAI product, CreditAI here and our AI framework here.

Responsibilities

• Apply strong problem-solving and critical thinking skills to break down complex, ambiguous requirements into clear, implementable technical components and system designs.
• Design, build, and maintain AI-powered and data-driven systems with a focus on modern language and multimodal models, including LLM-driven applications, RAG pipelines, and agentic workflows.
• Evaluate and productionize commercial and open-source LLMs, choosing appropriate models, tools, and techniques for each use case. Develop multi-step agentic workflows that incorporate tools, external data sources, memory, and control logic.
• Manage the orchestration of production LLM workflows and agentic systems, ensuring reliability and efficiency through prompt routing, state management, retries, fallbacks, and error handling. Design, test, and iteratively refine prompts and system instructions using prompt engineering and tuning techniques to improve model reliability, accuracy, and task performance.
• Maintain production-grade code and services with automated monitoring and performance tracking, using metrics and alerts to guide continuous improvements in models, prompts, and pipelines.
• Apply systems thinking to design and optimize AI and LLM systems, balancing quality, scalability, latency, cost, and operational complexity, while implementing efficiency improvements using model selection, prompt design, batching, caching, and retrieval strategies.
• Define and implement evaluation and observability frameworks for AI systems, including automated testing, task-specific benchmarks, regression testing for prompts, human-in-the-loop validation, and performance monitoring.
• Build and integrate AI models into backend systems and APIs to support both real-time and batch inference, ensuring solutions are production-ready, scalable, and efficient.
• Apply NLP and ML techniques to tasks such as information extraction, semantic search and retrieval, text classification, summarization, and reasoning over text and documents.
• Collaborate closely with engineering and infrastructure teams to deploy solutions using containerized and cloud-based environments (e.g., GitHub, Docker, AWS), applying modern deployment and infrastructure practices.
• Collaborate with product managers, business stakeholders, and domain experts to translate complex, ambiguous business problems into actionable technical solutions, and communicate progress, trade-offs, and outcomes to relevant stakeholders.
• Continuously learn and adapt to advancements in NLP and Generative AI to ensure solutions remain innovative and effective.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
• 3+ years of experience as a Data Scientist, Machine Learning Engineer, or applied AI practitioner, with a strong foundation in computer science, algorithms, and software development. 
• Advanced programming skills in Python, with experience building production-grade systems beyond research or experimentation.
• Solid understanding of machine learning and applied AI concepts, with experience taking solutions from prototype to production.
• Hands-on experience designing, building, and deploying LLM-driven or GenAI applications, including familiarity with vector databases, embeddings pipelines, or semantic search systems.
• Practical experience with cloud-based deployments and infrastructure tools (e.g., AWS, Docker, GitHub) and an understanding of modern DevOps practices, containerization, orchestration, caching strategies, and cost-aware design.
• Strong problem-solving skills and systems thinking, with the ability to balance trade-offs across model quality, scalability, inference latency, cost, and operational complexity.
• Ability to interpret and implement research ideas and algorithms, actively contributing to research and development initiatives while translating them into production solutions.
• Excellent communication and collaboration skills, with experience working closely with product managers, engineers, and domain experts to deliver actionable technical solutions.
• Passion for learning and staying current with the rapidly evolving AI/ML landscape, including emerging best practices for GenAI applications.
• Strong ownership and initiative, with the ability to independently drive projects from problem definition to delivery, while being a team player and contributing to the overall success of the data science team.

At Octus, we consider a range of factors in connection with compensation decisions, including experience, skills, location, and our business needs and limitations. As a result, compensation may vary within and across similar roles and positions. Please note that the salary range information below is a good faith estimate for this position and actual compensation for any individual may fall outside this range if warranted by the circumstances applicable to that individual. If we identify a role that would be suitable for a broader range of skills and experience such that we would consider hiring at multiple levels then the range listed below may reflect that breadth.

The salary range estimate for this position is $130,000-$145,000.

The actual compensation will be at Octus’ sole discretion and will be determined by the aforementioned and other relevant factors. This position is eligible for a performance-based annual bonus.

Please note: This position is not eligible for immigration sponsorship. 

Company Overview

We are the better way to work in finance. As private equity’s value creation partner, we sit at the heart of PE—where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, we help clients drive value—supporting the office of the CFO to drive end-to-end value creation.

If you crave challenging work and are looking to grow, come solve complex issues alongside 1,600+ finance & technology experts in a supportive, collaborative environment.

Backed by premier private equity firms and headquartered in New York with 11 offices around the globe, we are a high-growth, entrepreneurial firm looking for people who want to be part of building something great. Come make your mark. 

AI Governance Team, Responsible AI/ML Data Scientist

We are seeking a Vice President, Responsible AI/ML Data Scientist on our AI Governance Team who combines technical expertise in Data Science and Machine Learning with a strong advisory lens to help drive trustworthy AI solution development across Accordion's practices. In this role, you will serve as an internal subject matter expert, embedding responsible AI principles and controls across the AI development lifecycle to drive validated, explainable, compliant, and auditable AI solutions. Drawing on your experience with AI policy, regulatory standards, and testing methodologies, you will translate governance requirements into actionable and testable playbooks that teams across the business can apply consistently. This position is critical to co-designing AI solutions with embedded Responsible AI practices aligned to Accordion's AI Governance principles. As Accordion scales AI capabilities across its practice areas, this role will provide the validation rigor and oversight needed to ensure we develop AI solutions our clients and firm can trust.

This position must be based in our New York City, Chicago, or San Francisco office and is a hybrid role with the flexibility to work remotely 2 days a week.

This position is not eligible for immigration sponsorship.

What You’ll Do:

Governance & Technical Standards

• Translate AI governance, regulatory, and compliance requirements into testable operational Responsible AI playbooks with quantifiable standards for transparency, accuracy, fairness, privacy, and accountability — tailored to Accordion's AI solutions and business model
• Develop frameworks and guidance supporting systematic evaluation, testing, and risk mitigation that enable auditability, lineage, and transparent decision records
• Build evaluation frameworks that guide data scientists and engineering teams in developing, testing, and monitoring AI systems requirements; define mitigation strategies, detection methodologies, and acceptability thresholds
• Develop and implement testing principles for bias detection, model robustness evaluation, privacy preservation, and AI/privacy regulation alignment across different AI architectures and data types
• Establish benchmarks and governance processes aligned with industry standards (e.g., NIST AI RMF, EU AI Act, ISO 42001) that enable auditable AI transparency and explainability against applicable regulations and Accordion standards

Testing & Validation

• Collaborate with data science and engineering teams across our AI solution practices to embed responsible AI controls throughout the development lifecycle
• Develop repeatable testing and validation playbooks and evaluation frameworks for use across practice areas
• Pressure test AI solutions for accuracy, reliability, and trustworthiness, including output anomaly detection, logging, and observability mechanisms
• Develop methods to produce model cards and support audit trails for outputs across Accordion's business practice pillars and Client solutions

Cross-Functional Advisory

• Act as the firm's internal subject matter expert on Responsible AI topics including algorithmic fairness, transparency, privacy preservation, safety protocols, and risk mitigation strategies across the AI lifecycle
• Partner with Legal/Privacy/Risk, Technology, and D&A teams to translate regulatory requirements into actionable and measurable controls and governance structures
• Collaborate with teams developing AI solutions on risk-informed design decisions — advising on model selection, testing approaches, and appropriate guardrails
• Establish Responsible AI frameworks and operational playbooks that demonstrate alignment with evolving AI regulations (e.g., EU AI Act, GDPR, CCPA) and sector-specific requirements
• Monitor emerging AI regulations, industry standards, and responsible AI methodologies — translating insights into actionable internal design guidance
• Where applicable, support higher-risk or higher-profile engagements and AI product development where rigorous testing and evaluation of AI solutions is required

You Have:

• 5+ years of hands-on experience in data and ML, including developing, deploying, and evaluating solutions using a range of skillsets (e.g., statistics, machine learning, NLP, data visualization)
• Bachelor's degree in Computer Science, Data Science, Information Systems, Mathematics, Engineering, Statistics, or a related quantitative field
• Proven technical experience in regulated or complex industries with demonstrated collaboration across Legal/Risk/Compliance, Security, and AI/ML engineering teams
• Track record of operationalizing responsible AI through technical development frameworks, testing protocols, and quantitative evaluation that embed fairness, transparency, and accountability into production AI systems
• Strong programming proficiency in Python, R, or similar languages, with experience in ML frameworks and responsible AI tooling
• Understanding of AI and privacy regulations and standards — including EU AI Act, NIST AI RMF, GDPR, CCPA, and ISO 42001 — and the ability to translate regulatory mandates into technical controls
• Exceptional communication skills with the ability to convey complex technical and regulatory considerations to non-technical stakeholders, including leadership and cross-functional teams

Preferred Qualifications:

• Advanced degree (Master's) in Computer Science, Data Science, Statistics, Information Systems, Engineering, or a related quantitative discipline
• Consulting experience at a leading management or professional services firm
• Direct experience working with Legal/Privacy/Compliance teams on AI, data governance, or emerging technology matters
• Familiarity with responsible AI tooling, red teaming platforms, or model evaluation frameworks

You Are:

• Energized by ensuring AI systems that solve complex business challenges are built responsibly — with fairness, accuracy, transparency, and privacy by design embedded from the start
• A cross-functional collaborator who navigates matrixed organizational dynamics, working seamlessly with Legal/Risk/Compliance, Technology/Engineering, and business teams to establish AI principles, governance playbooks, and technical roadmaps
• A clear communicator who can translate AI governance concepts and technical risk findings into language that resonates with non-technical stakeholders and leadership
• A proactive problem-solver who thrives on staying ahead of AI governance expectations and emerging regulations — and can turn frameworks into practical controls
• An adaptive contributor who embraces the pace of consulting, working across diverse sectors and practice areas

The annual salary for this role ranges from: $160,000 to $175,000 + benefits. This role is also eligible for bonuses, based on individual and company performance. Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to, geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity. 

Accordion is an Equal Opportunity Employer. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills.  We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical​​​ condition, pregnancy, genetic information, gender, sexual orientation, gender identity or ​expression, veteran status, or any other status protected under federal, state, or local law.

Please note that as per Accordion policy, we do not accept unsolicited resumes from third-party recruiters unless such recruiters are engaged to provide candidates for a specified opening and in alignment with our inclusive diversity values. Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that Accordion will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity.

#LI-HA1

Please note: This position is not eligible for immigration sponsorship. 

Company Overview 

We are the better way to work in finance. As private equity’s value creation partner, we sit at the heart of PE—where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, we help clients drive value—supporting the office of the CFO to drive end-to-end value creation. 

If you crave challenging work and are looking to grow, come solve complex issues alongside 1,600+ finance & technology experts in a supportive, collaborative environment.

Backed by premier private equity firms and headquartered in New York with 11 offices around the globe, we are a high-growth, entrepreneurial firm looking for people who want to be part of building something great. Come make your mark.  

AI Governance Team, AI Risk and Operations 

We are seeking a Vice President, AI Risk and Operations on our AI Governance Team who will play a critical role in maturing and scaling Accordion's enterprise-wide AI Governance risk and operations program. In this role, you will serve as an internal governance practitioner and cross-functional advisor, embedding AI governance policies, procedures, and controls across our operating model for internal AI solutions — with the expectation that this scope will evolve to include client-facing engagements over time. Drawing on your experience with AI policy, frameworks, and risk program design, you will translate complex governance requirements into actionable, scalable implementation systems. This position requires seamlessly bridging the gap between cutting-edge AI development and responsible governance, partnering with senior stakeholders across Practices, Technology, Legal, and Emerging Technology to enabling trusted use and development of AI.  

This position can be based in our New York City or Chicago offices and is a hybrid role with the flexibility to work remotely 2 days a week. Ideal candidates should be local to the desired location. 

This position is not eligible for immigration sponsorship. 

What You’ll Do 

Governance & Frameworks 

• Enhance the enterprise AI Risk Management Framework, including AI use case and supply chain inventory management, use case risk classification taxonomies, and risk-tiering mechanisms aligned to internal standards and emerging regulatory expectations 
• Design and operationalize scalable, end-to-end governance processes across the AI lifecycle  
• Build and maintain AI governance playbooks, risk appetite frameworks, and responsible AI standards  
• Identify opportunities to strengthen controls and oversight through process automation, tooling improvements, and governance workflow optimization 

Cross-Functional Collaboration 

• Partner with Legal, Technology, and Consulting Practice Groups to embed explainability, bias mitigation, and ethical AI principles directly into use case development and delivery workflows 
• Manage the firm's AI Governance obligations and processes end-to-end, including supporting AI and related vendor management supply chain   
• Coordinate with Legal teams to ensure AI systems meet applicable jurisdictional and regulatory requirements, including proactive engagement on evolving global AI legislation 
• Facilitate stakeholder working groups, cross-functional governance forums, and executive communications on AI risk priorities, testing outcomes, and documentation requirements 
• Maintain the internal AI risk register and escalate issues through appropriate governance channels 
• Liaise with Technology and Practices to ensure data lineage and quality standards are met for AI solutions 

Monitoring & Oversight 

• Establish, track, and report on key risk indicators (KRIs) for AI systems in partnership with the Responsible AI Data Scientist and AI Governance Lead, across internal and client-facing AI solutions and products 
• Monitor alignment with global AI regulatory frameworks (e.g., EU AI Act, NIST AI RMF) and support responses to regulatory inquiries, audits, or reviews 
• Evaluate risks associated with third-party and vendor AI solutions across the full sourcing lifecycle — including procurement, onboarding, integration, and ongoing performance oversight — and identify operational guardrails and controls 
• Assess design and operating effectiveness of AI controls against internal policy and external standards, especially for AI, privacy, and industry regulations, under Legal's guidance 
• Produce high-quality audit findings reports with clear recommendations for remediation and monitor implementation of agreed action plans 
• Stay current on AI regulation, industry standards, and emerging best practices 

Internal Enablement (Evolving) 

• Support the development of AI Governance updates, training/awareness programs, and culture-building initiatives to embed responsible AI practices across the organization 
• Contribute to Accordion's Trustworthy AI methodology and internal IP development 
• Over time, support client-facing AI governance assessments, risk remediation roadmaps, and responsible AI frameworks as the program matures and scope expands  

You Have 

• Bachelors degree in Finance, Business Administration, or related field  
• 7+ years’ experience in Risk Operations, Privacy Programs, or PMO/IMO, with at least 3 years directly focused on AI governance or oversight - preferably within professional services, consulting, or PE-backed environments. 
• Demonstrated experience leading complex, multi-jurisdictional integrations and/or global expansion programs, with a track record of building and running structured governance. 
• Strong knowledge of AI and privacy regulations and standards — including EU AI Act, NIST AI RMF, GDPR, CCPA, and ISO 42001 — and the ability to translate regulatory mandates into operational controls 
• Strong grasp of AI/ML technical risks (e.g., bias, model drift, robustness) and associated controls 
• Strong familiarity with regulatory and operational considerations relevant to cross-border professional services (e.g., data/privacy, employment, tax, licensing), and the ability to operationalize them into business processes. 
• Exceptional communication skills with the ability to convey complex technical and regulatory considerations to non-technical stakeholders, including leadership and cross-functional teams 
• Comfortable operating in a high-growth, entrepreneurial environment, with a bias toward structure, scalability, and execution discipline. 
• Strong analytical and problem-solving skills  
• AI usage and familiarity required 
• Excellent attention to detail and organizational skills   
• Ability to adapt to changing priorities and work effectively in a dynamic environment   

Preferred Qualifications 

• Experience in Data Risk Management or direct collaboration with AI/ML development teams 
• Familiarity with AI risk management platforms or tools for model monitoring, documentation, and compliance reporting 
• Experience designing training, awareness, or enablement programs focused on AI risk, model governance, or responsible AI practices 
• Certifications in risk, privacy, or compliance (e.g., CIPP, CIPM, CRISC, CRCM) 
• Passion for AI and a desire to build a world-class risk management function  

You Are 

• Energized by building and scaling governance programs that make AI deployment safer, more trustworthy, and more compliant — across a dynamic, fast-moving consulting environment 
• A cross-functional collaborator who navigates matrixed organizational dynamics, working seamlessly with Legal/Risk/Compliance, Technology/Engineering, and business teams — and can drive decision making and ownership across diverse stakeholders 
• A clear communicator who can translate AI governance concepts, regulatory requirements, and risk findings into actionable guidance for business, technical, and executive audiences 
• A proactive problem-solver who thrives on staying ahead of AI governance expectations and emerging regulations, and can turn frameworks into operational reality 
• An adaptive contributor who embraces the breadth and pace of consulting, working across diverse sectors and practice areas 

The annual salary for this role ranges from: $130,500 to $145,000 + benefits. This role is also eligible for bonuses, based on individual and company performance. Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to, geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity.  

Accordion is an Equal Opportunity Employer. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills.  We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical​​​ condition, pregnancy, genetic information, gender, sexual orientation, gender identity or ​expression, veteran status, or any other status protected under federal, state, or local law. 

Please note that as per Accordion policy, we do not accept unsolicited resumes from third-party recruiters unless such recruiters are engaged to provide candidates for a specified opening and in alignment with our inclusive diversity values. Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that Accordion will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity. 

#LI-HA1

The Team

Our AI research team sits at the heart of our mission to unlock new dimensions of biological understanding. You will leverage state-of-the-art AI to accelerate discovery and drive transformative insights in biology—developing novel AI models purpose-built for biological research, engineering robust systems that enable breakthrough science at unprecedented scale, and translating these advances into practical tools that empower researchers worldwide.

Our approach is comprehensive and integrated, bringing together world-class AI model development, exceptional engineering talent, high-quality biological sciences and data, powerful computing infrastructure, and strategic partnerships. Success requires excellence across five interconnected pillars: training frontier AI models specifically for biology; building engineering systems that maximize research velocity and efficiency; executing a sophisticated biological and data strategy that fuels AI development; operating a world-class AI compute platform; and creating impactful products that transform AI capabilities into accessible scientific tools.

The Opportunity

This is an opportunity to shape the future of biological research by pushing the boundaries of what AI can achieve in science. You’ll work alongside leading experts in AI and biology, with the resources and mandate to tackle some of the most important questions in human health — advancing frontier AI research, accelerating engineering velocity, connecting rich biological data to AI systems, enabling reliable compute across environments, and translating models and data into usable, scalable applications that drive scientific impact.

The role is part of the Data team, which is responsible for maximizing the speed, agility, and capability of biological AI research by connecting public data resources and Biohub's experimental platforms to AI systems. The data that trains biological frontier models comes in dozens of modalities (sequences, images, spatial coordinates, time series, molecular structures, metadata, publication artifacts, …) each with its own noise characteristics, biases, and information content. The question of how AI can reason across these diverse descriptions of biology to answer specific experimental questions is one of the core challenges in biology.

You will define the data approach to train our reasoning system. To do so, you will operate with broad scope and high autonomy, influencing roadmap decisions across teams while mentoring individual contributors. Success in this role means scaling data systems that are not only large, but adaptive, interpretable, and scientifically grounded, accelerating progress toward robust biological frontier models and ultimately advancing human health. We're looking for data scientists who can work at this frontier: people who understand scientific experimentation deeply, think creatively about data representations and tokenization strategies, and have experience building reasoning systems. You'll work directly with experimental and computational scientists, data scientists and AI researchers to define what the models see and how they see it, and data engineers to make this work at scale. This is a role for someone who wants to invent the methods that make biological frontier models possible.

What You'll Do

• Design reasoning tasks for our models.
• Build training datasets that capture biology experiments, including experimental design, hypothesis generation, evidence interpretation, and scientific inference
• Design training strategies that teach reasoning capabilities, working closely with AI Research to translate data approaches into model behavior
• Create evaluation and benchmarking frameworks that measure reasoning quality and analyze model behavior to influence the next set of evaluations, environments, and data.
• Partner with Scientific Data Strategy and Data Engineering to identify and acquire source materials (literature, protocols, experimental records) that contain reasoning signal
• Set technical direction for reasoning data efforts, influencing priorities and mentoring other data scientists working in this area

What You'll Bring

• PhD in machine learning, computational biology, or another quantitative field
• Hands on understanding of how scientists reason across diverse experimental systems as obtained from hands-on experience with laboratory science in biology, biochemistry, or chemistry
• Experience curating or creating training data and tokenization strategies for reasoning models (RLVR, synthetic reasoning traces, RL with Tool Use)
• Track record of novel methodological contributions (publications, open-source tools, or production systems)
• Familiarity with evaluation methodology for complex, open-ended tasks where ground truth is ambiguous
• Strong computational skills (Python, data processing at scale); ability to work with large text corpora and structured data
• Creative, first-principles thinking about how to structure data for learning

Compensation

The Redwood City, CA & New York City, NY base pay range for a new hire in this role is $214,000 - $268,000. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. 

This position may be eligible to participate in Biohub's discretionary annual performance bonus program. Bonus eligibility and targets are determined in accordance with Biohub's total rewards philosophy and may vary by role.

Better Together

As we grow, we’re excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team’s manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process.

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

If you’re interested in a role but your previous experience doesn’t perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role.

#LI-Hybrid 

Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. 

Our research spans:

• Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. 
• Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science
• Imaging foundation models trained across the world's largest microscopy datasets
• Reasoning and agentic systems that connect frontier LLMs with biological foundation models
• Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights
• Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled

Join Our Team!

As a Research Scientist, you'll build the models and systems that define what AI can do in biology: foundation models, reasoning, reinforcement learning, and multi-agent systems at frontier scale.

What You'll Do

• Build on and advance the AI systems at the frontier of biology to accelerate science
• Design novel model architectures and scale pre-training pipelines for our biology foundation models.
• Build post-training systems that drive biological discovery: RL, reward modeling, reasoning, and multi-agent orchestration for long-horizon scientific tasks.
• Design evaluation frameworks and AI systems with scientists across Biohub and beyond, grounded in real biological outcomes and real world impact.
• Engage the wider scientific community through impactful publications, open-source releases, and collaborations worldwide.

What You'll Bring

• PhD in computer science or a related field, or equivalent experience
• Significant experience building and scaling deep learning systems, ideally in research-driven environments
• Strong engineering and scientific fundamentals
• Comfort with ambiguity, rapid iteration, and loosely-defined research problems
• Strong communication skills across technical and scientific audiences

Compensation

The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is $214,000 to $375,000 annually. Final compensation is based on the level at which you are hired. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time! 

#LI-Hybrid 

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The Fraud Data Science team safeguards Robinhood and its customers by detecting and preventing fraud and abuse across our platform. We leverage machine learning and analytics to combat malicious behavior in real time, supporting a safe and trusted experience for all users. Our work has a direct impact on customer security, company risk posture, and regulatory compliance.

As a Senior Data Scientist on the Fraud team, you will own the design and deployment of ML solutions that proactively surface suspicious activity, reduce financial loss, and improve fraud detection precision. You’ll collaborate closely with engineering, product, risk, and compliance partners to influence system architecture, shape policy through data, and enhance the safety and integrity of our platform.

This role is based in our Menlo Park office, with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams. 

What you’ll do

• Design and deploy fraud detection models to protect Robinhood users and assets in real time
• Analyze behavioral data to uncover emerging fraud vectors and support rapid incident response
• Develop robust data pipelines and monitoring systems to ensure model accuracy and reliability
• Partner with engineering and product teams to implement safeguards and user-facing features
• Guide experimentation strategy and contribute to long-term fraud prevention roadmap

What you bring

• 5+ years of experience in data science or applied ML, with a focus on fraud detection or risk mitigation
• Advanced proficiency in Python and SQL; experience with ML frameworks like XGBoost, LightGBM, or TensorFlow
• Strong statistical acumen with experience in anomaly detection, pattern recognition, and A/B testing
• Excellent communication skills and ability to influence decision-making across technical and non-technical audiences
• A collaborative mindset and proactive approach to navigating ambiguity in fast-paced environments

What we offer

• Challenging, high-impact work to grow your career.
• Performance-driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching.
• Best-in-class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents.
• Lifestyle wallet — a highly flexible benefits spending account for wellness, learning, and more.
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits.
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.

Small business owners trust Gusto with something fundamental: making sure their people get paid, on time, every time. As a Principal Data Scientist on the Payments team, you'll sit at the intersection of financial infrastructure and data science — translating the complexity of a high-scale payments ecosystem into the insights and models that shape how we build, operate, and improve our payments products. 

In this role you will work closely with our Product, Engineering, Design, Finance, and other Data teams to become an expert in the data for your domain, define and track metrics that help us understand our business performance, and dive deep into our payments data to deliver insights and answer questions. You’ll also integrate AI-assisted practices to accelerate analysis, enhance rigor, and expand the reach of insights across Gusto. 

This is a role for someone who's equally comfortable diving into ledger-level transaction data and standing in front of a payments engineering team to influence architecture decisions. If you want your work to directly protect and improve the financial lives of hundreds of thousands of small businesses, this is the role.

Here’s what you’ll do day-to-day:

• Own payments intelligence end-to-end: Design and maintain measurement frameworks for payment success rates, failure root causes, retry strategies, and settlement timing — becoming the authoritative source of truth for payments health across the org.
• Partner with payments engineering: Embed with backend engineering teams to understand infrastructure constraints and data availability, translate that knowledge into well-scoped experiments, and influence roadmap prioritization with data-backed recommendations.
• Drive experimentation at scale: Design and analyze A/B tests and quasi-experiments across payment flows — including retry logic, routing decisions, and failure recovery — ensuring statistical rigor even in low-conversion, high-variance payment environments.
• Build predictive models: Develop and deploy models for payment failure prediction, risk scoring, and anomaly detection that operate at Gusto's transaction volume, partnering with Machine Learning Engineers and Product Engineers on productionization and monitoring.
• Build AI-native data products: Go beyond using AI to go faster; build data products and automated workflows with AI as a core component. Know what "good" looks like for a clustering model or automated workflow, when to trust the output, when to challenge it, and how to build guardrails that let the team scale AI use responsibly in a regulated financial context.
• Own the business narrative: Be the person payments product and engineering leadership calls before an exec review, not after. Comfortable defending methodology on ARR projections, CX attribution, and cost savings in a room with skeptics. Translate nuanced statistical results and model outputs into clear, actionable narratives for payments engineers, product managers, finance partners, and executive leadership.

Here’s what we're looking for:

• 10+ years of experience in Data Science or a closely related quantitative role at a product-focused software company, with meaningful exposure to payments, fintech, or financial infrastructure.
• Deep payments domain knowledge: Hands-on experience with payments infrastructure: ACH, card networks, bank integrations, settlement flows, or equivalent, with a strong intuition for how backend systems shape data availability and quality.
• Strong SQL and Python skills, with experience querying large-scale transactional datasets and building reproducible analytical pipelines.
• Proven experimentation and causal inference expertise: Ability to design rigorous experiments in challenging payment contexts (rare events, high variance, non-stationarity) and communicate trade-offs clearly.
• AI-native mindset: Has built data products and analytical workflows with AI as a core component, not just used AI to go faster. Knows what "good" looks like for a clustering model or automated workflow, when to trust the output, when to challenge it, and how to build guardrails that scale AI use responsibly in a financial context.
• Cross-functional influence: Track record of collaborating with and influencing backend engineers and technical stakeholders, not just product and business partners.

Our cash compensation amount for this role is targeted at $184,000/yr - $210,000/yr in Denver, and $217,000/yr - $250,000 for San Francisco and New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

About the role:

We are seeking an exceptional Data Scientist to join our Garner Health Intelligence team to help build and evolve our provider data insights. You’ll work with our clinical researchers, data scientists, and data engineers to improve the rules and models we use to evaluate provider performance – e.g., answering questions like, how do we know which providers deliver better surgical outcomes or what data concepts do we need to build out to improve our analysis. Your work will directly impact our member recommendations and provider-facing data products.

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Partner with data scientists and clinical experts to shape how we define provider performance metrics, apply statistical rules, and model provider behavior
• Drive the evolution and construction of tools we use to write, assess, and combine rules measuring provider behaviors
• Build and maintain data-driven algorithms, including AI and machine learning models, to improve our provider recommendations estimates.
• Design scalable data pipelines that ingest, clean, and enrich healthcare provider data.
• Collaborate cross-functionally with product, engineering, research, and operations teams to translate business needs into data-driven solutions.

The ideal candidate has:

• 2-5 years professional experience in data science, product analytics, and/or healthcare
• A demonstrated ability to independently solve difficult problems and deliver high quality results to senior stakeholders
• Strong Python and SQL skills
• A willingness to “roll up your sleeves” and do whatever is necessary to ensure company success
• Experience working in a rapidly evolving startup environment
• A desire to be a part of our mission to improve the U.S. healthcare system
• A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Technologies we use: 

• Python, TypeScript, React, NodeJS, Kubernetes, Istio, Postgres, ElasticSearch, NATS, AWS, Terraform

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $160,000 - $185,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more.

About the role:

We are seeking an exceptional Staff Data Scientist to join our Garner Health Intelligence team to help build and evolve our provider data insights. You’ll work with our clinical researchers, data scientists, and data engineers to improve the rules and models we use to evaluate provider performance. Your work will directly impact our member recommendations and provider-facing data products.

You will also be a technical leader, raising the bar for how we conduct analysis, build models, and deploy data science code by improving standards and mentoring team members.

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Partner with product managers and clinical experts to lead projects shaping how we define and measure provider cost and quality
• Drive the evolution and construction of tools we use to write, assess, and combine rules measuring provider behaviors
• Build and maintain data-driven algorithms, including AI and machine learning models, to improve our provider recommendations estimates
• Serve as a subject matter expert for statistical modeling, improving methods for evaluating current models and future changes
• Partner with data engineers to design scalable data pipelines that ingest, clean, and enrich healthcare provider data
• Collaborate cross-functionally with product, engineering, research, and operations teams to translate business needs into data-driven solutions
• Define data science standards and best practices, and mentor team members to raise the technical capabilities of the data science team

The ideal candidate has:

• 8+ years professional experience in data science, product analytics, and/or healthcare
• A demonstrated ability to independently solve difficult problems and deliver high quality results to senior stakeholders
• Strong Python and SQL skills
• Expertise in statistical modeling techniques and a track record of building impactful predictive models
• A willingness to “roll up your sleeves” and do whatever is necessary to ensure company success
• Experience working in a rapidly evolving startup environment.
• A desire to be a part of our mission to improve the U.S. healthcare system
• A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Technologies we use: 

• Python and Jupyter Notebooks, SQL (Snowflake), Argo, DBT, AWS, Terraform

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $263,000 - $300,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more.

About the role:

We are seeking an exceptional Applied Scientist to work on core search systems that power our product, with a particular focus on provider selection and recommendation algorithms. You will play a pivotal role in shaping how Garner selects high-value providers for members by developing the evaluation frameworks, objective functions, and algorithms that drive our search experience.

This role is ideal for someone who is excited by ambiguous, high-impact problems and can translate messy real-world constraints into robust algorithmic systems. You will partner closely with Product and Engineering to design and deliver production algorithms that balance provider quality, affordability, and access.

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Build and own the algorithms that power Garner’s search
• Design objective functions, scoring frameworks, and decision logic that balance competing goals such as quality, cost, and access
• Translate ambiguous product and business problems into rigorous algorithmic approaches that can be deployed in production
• Partner with engineers to productionize systems at scale
• Identify where applied machine learning, optimization, heuristics, or rules-based methods are the right tool for a problem
• Develop a strong understanding of the healthcare economy

The ideal candidate has:

• 4+ years of experience using Python and SQL to build production, data-driven algorithms
• Strong applied problem-solving skills, with the ability to define good metrics and design algorithms under ambiguity
• Strong judgment in choosing between statistical models, heuristics, optimization approaches, and simpler algorithmic methods depending on the problem
• Strong communication skills and the ability to synthesize complex algorithmic ideas for business and technical stakeholders
• A desire to be a part of a high-performing, mission-driven team that operates with urgency, a strong sense of individual accountability, and a commitment to authentic feedback

Technologies we use: 

• Python, SQL, AWS, Snowflake, pandas, XGBoost

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $200,000 - $240,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k) with company match, flexible spending accounts, Teladoc Health and more.

About the role:

We are seeking an exceptional Data Scientist II to join our Central Data team. This role will report to the Director, Data Science & Analytics. This role is critical to shaping Garner’s Product & Growth strategy - from evaluating the impact of our products to building models that drive engagement. You will collaborate with data, product & engineering to improve the effectiveness of Garner’s member facing product – e.g., answering questions like, “what features of our product drive engagement?”, “What is the best time of day and channel to outreach to members?”, or “What data concepts do we need to build out to improve our understanding of what drives someone to engage with Garner?” Your work will directly impact our member-facing products and inform strategic product decisions.

Where you will work:

This role will be based in our New York City office. You must be willing to work in the office 3 days per week on Tuesday, Wednesday and Thursday. 

What you will do:

• Identify and measure success of product efforts through goal setting, AB testing, forecasting, and monitoring of key product metrics to understand trends
• Own projects end to end including problem definition, implementation, validation, & iteration 
• Research, develop, and maintain data pipelines, statistical models, and advanced analytical solutions, including AI & machine learning models 
• Collaborate cross-functionally with data scientists, product leaders, engineering & operations teams  to shape how we define product & member engagement metrics, apply statistical rules, and model member behavior
• Synthesize & present your work to Garner leadership to drive company level decisions
• Contribute to improving data science practices, tooling, and documentation

What you will bring to the team:

• 2+ years of experience in data science, applied analytics, or a related quantitative field (industry, academia, or both)
• 2+ years of experience using SQL and Python and/or R to query, analyze, and manipulate data
• 2+ years of experience building data models, using more advanced analytics methods, statistical modeling, and/or data processing
• Excellent verbal and written communication skills, with ability to present information and analysis results effectively
• A demonstrated ability to independently solve difficult problems and deliver high quality results to senior stakeholders
• A willingness to “roll up your sleeves” and do whatever is necessary to ensure company success, ideally in a startup environment
• A desire to be a part of a high-performing, mission-driven team that operates with intense urgency, a strong sense of individual accountability, and a commitment to authentic feedback

This is a unique opportunity to join a fast-growing company in a transformative role, helping shape the future of healthcare.

Please note: we are unable to sponsor or take over sponsorship of an employment visa at this time.

Compensation Transparency:

The target salary range for this position is $158,000 - $175,000. Individual compensation for this role will depend on various factors, including qualifications, skills, and applicable laws. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including but not limited to: flexible PTO, Medical/Dental/Vision plan options, 401(k), Teladoc Health and more.

Data Scientist, Developer Productivity

About the role

As part of our growing Data Science and Analytics team, you'll play an instrumental role in Anthropic's mission of building safe and beneficial AI by driving data-informed decision making across the company. This role sits at the intersection of data science, developer experience, and AI tooling — and offers the unusual opportunity to study frontier AI usage from the inside, with the builders themselves as your users.

You'll define how Anthropic understands and improves developer productivity — both through classic software engineering effectiveness measures and through the emerging challenge of understanding AI-augmented development workflows. You'll own the quantitative foundation for how Anthropic's engineers build: what slows them down, what accelerates them, where tooling investments pay off, and how AI-assisted development is changing the shape of engineering work. Your analyses will directly inform infrastructure priorities, tooling roadmaps, and how we think about scaling engineering output as Anthropic grows.

You've worked in cultures of excellence in the past, and are eager to apply that experience to help shape the cultural norms and best practices of a growing data science team as Anthropic continues to scale.

Key responsibilities

• Define key metrics, build measurement frameworks, and maintain core reporting to evaluate developer productivity and engineering effectiveness
• Deep dive into product and user data to derive actionable insights, size opportunities, and influence roadmaps through clear recommendations
• Develop hypotheses and apply rigorous causal inference methods — controlled experiments, synthetic controls — to make actionable recommendations
• Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions
• Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes
• Present complex analyses and recommendations to both technical and non-technical stakeholders
• Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration as our products grow

Minimum qualifications

• Working expertise with Python and SQL
• Working expertise with data visualization tools
• Hands-on experience with experimental design, causal inference, statistical modeling, and A/B testing frameworks
• Strong written communication and presentation skills
• Track record of translating complex data into clear, actionable insights for both technical and business stakeholders

Preferred qualifications

• 7+ years of experience in data science or analytics roles
• Direct experience working with developer productivity, infrastructure, performance, or platform teams in rapidly scaling environments
• Deep understanding of distributed systems, cloud infrastructure, and performance engineering, with experience analyzing large-scale system metrics
• Experience applying experimental design and causal inference methods in high-scale technical environments
• Comfort with ambiguity and a track record of creating clarity and driving progress in fast-moving environments
• Experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem
• Passion for Anthropic's mission of building helpful, honest, and harmless AI

Deadline to apply: None. Applications are reviewed on a rolling basis.

The Data Science team at Asana is pivotal in fulfilling our mission by fostering a data-driven approach in shaping both our product and business strategies. In your role on the Marketing Data Science team, you will be the deepest technical expert responsible for using data and scientific techniques to design and build scalable, state-of-the-art solutions to enhance Asana’s marketing effectiveness. You will drive the technical roadmap for data science, collaborating with marketing leadership and the broader Asana data community to uncover new opportunities. You will provide technical leadership and hands-on mentorship, elevating the team's technical bar and influencing overall business strategy through best-in-class modeling and experimental design.

This role is based in our New York City office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements.

What you’ll achieve:

• Architect, design, and lead the technical execution for the Marketing Data Science roadmap, serving as the Solution Architect for all core projects including Media Mix Modeling (MMM), User Lifetime Value, Causal Inferences, Multi-touch Attribution, and Spend Optimization engines.
• Act as the primary technical subject matter expert for the Marketing Data Science team, setting the technical bar for modeling quality, code rigor, data pipeline architecture, and solution scalability.
• Collaborate with marketing leadership to pinpoint how data science can be further integrated into Asana's business approach.
• Provide hands-on technical mentorship and guidance to a team of data scientists at varying levels, helping them navigate complex modeling challenges, choose appropriate methodologies, and establish robust ML Ops.
• Develop and standardize MLOps tooling and processes that enable the team to deploy, monitor, and maintain multiple models in production efficiently and reliably.
• Research, prototype, and advocate for emerging capabilities and state-of-the-art models in the marketing data science space, demonstrating their potential benefits and leading their implementation.
• Take on a technical leadership role within the broader Asana Data Community, interacting with Data Engineering and Platform teams to influence the data and MLOps infrastructure required to support marketing data products.

About you:

• Bachelor Degree in Math, Statistics, Computer Science, Engineering a related quantitative field, or equivalent experience
• 6+ years of experience in a data science role, with 2+ years dedicated to technical leadership and mentorship of other data scientists, successfully driving the architecture and execution of large-scale production data science projects
• 4+ years of experience collaborating with Marketing functions on deep technical projects, with extensive experience designing, implementing, and deploying marketing models (e.g. MMM, LTV, MTA, Uplift)
• Expert-level knowledge in advanced statistical modeling, causal inference, experimental design and analysis, and machine learning techniques relevant to marketing effectiveness
• Proven track record developing, deploying, and maintaining scalable production ML solutions and data products
• Technical Stack: Expert proficiency in SQL and Python. Experience with MLOps tools (e.g., MLFlow), statistical languages (e.g., R), and distributed data processing systems (e.g., Spark, Redshift) is a plus
• Demonstrates curiosity about AI tools and emerging technologies, with a willingness to learn and leverage them to enhance productivity, collaboration, or decision-making.

What we’ll offer:

Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.

For this role, the estimated base salary range is between $202,000 - $282,000. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified.

In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:

• Mental health, wellness & fitness benefits
• Career coaching & support
• Inclusive family building benefits
• Long-term savings or retirement plans
• In-office culinary options to cater to your dietary preferences 

These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your Talent Acquisition Partner to learn more about the total compensation and benefits for this role.

#LI-Hybrid #LI-AA1

The Opportunity:

Grow Therapy is seeking a Senior/Staff Data Scientist to deepen our understanding of our users and unlock the next era of our growth trajectory. In this role, you will drive insights that get more people successfully onboarded and retained in online therapy, shape how we build our mobile app and digital experiences, and lead the charge on how AI is used alongside therapy. You will also develop marketing and attribution models that help us reach patients more effectively and empower our providers’ success. Your work will directly influence product strategy, experimentation, and long-term investment decisions across the company.

What You’ll Be Doing:

• Lead experimentation and metric development to understand user behavior, optimize onboarding and retention, and guide product direction across web and mobile experiences.
• Build predictive, attribution, and behavioral models that inform marketing strategy, therapist-matching workflows, and future AI-powered features.
• Partner closely with Product, Engineering, Design, and Operations to identify opportunities, quantify impact, and drive roadmap decisions with clear, actionable insights.
• Develop dashboards, analytical frameworks, and self-serve tools that scale data access and empower teams to answer their own product questions.
• Shape Grow Therapy’s AI strategy by designing evaluation frameworks, running model-level experiments, and identifying where AI can improve the patient and provider experience.

You’ll Be a Good Fit If: 

• 5+ years of experience in a quantitative role applying analytics, experimentation, and modeling in a product-focused environment.
• Fluent in SQL and Python, with experience designing A/B tests, building models, and translating complex data into clear recommendations.
  Experience driving product or growth decisions through deep user understanding, funnel analytics, or marketplace dynamics.
• Comfort navigating ambiguity and working cross-functionally in fast-moving environments where data science directly influences strategy.
• Excited about improving access to mental health care and motivated by building AI-enhanced experiences that support therapists and patients.

Role Details:

Employment Type: Full Time, Exempt
Base Compensation: The base compensation range for this position is:

• Hybrid Commitment: $182,000–$228,000 USD USD Annually

This role can be hybrid (onsite from our NYC, San Francisco, or Seattle hub location three days per week: Tuesday, Wednesday, Thursday) or fully remote. Both arrangements include travel 2–3 times per year (e.g., company and department off-sites).

The Opportunity:

Grow Therapy is seeking a Staff Data Scientist to deepen our understanding of our users and unlock the next era of our growth trajectory. In this role, you will lead cross-functional, data-driven initiatives and drive insights that get more people successfully onboarded and retained in online therapy, shape how we build our mobile app and digital experiences, and lead the charge on how AI is used alongside therapy. You will also develop and own marketing and attribution models that help us reach patients more effectively and empower our providers’ success. Your work will shape product strategy, experimentation, and long-term investment decisions across the company.

What You’ll Be Doing:

• Lead experimentation and metric development to understand user behavior, optimize onboarding and retention, and guide product direction across web and mobile experiences.
• Build predictive, attribution, and behavioral models that inform marketing strategy, therapist-matching workflows, and future AI-powered features.
• Partner closely with Product, Engineering, Design, and Operations to identify opportunities, quantify impact, and drive roadmap decisions with clear, actionable insights.
• Develop dashboards, analytical frameworks, and self-serve tools that scale data access and empower teams to answer their own product questions.
• Shape Grow Therapy’s AI strategy by designing evaluation frameworks, running model-level experiments, and identifying where AI can improve the patient and provider experience.

You’ll Be a Good Fit If:

• 8+ years of experience in a quantitative role applying analytics, experimentation, and modeling in a product-focused environment, including leading high-impact, cross-functional data science or analytics initiatives.
• Fluent in SQL and Python, with deep experience designing A/B tests, building models, and setting a high bar for translating complex data into clear recommendations for stakeholders at all levels.
• Proven experience owning and driving product or growth decisions through deep user understanding, funnel analytics, or marketplace dynamics, and doing so through influence as well as hands-on work.
• Comfortable navigating ambiguity and working cross-functionally in fast-moving environments where data science directly influences strategy, and aligning multiple teams around shared metrics and insights.
• Excited about improving access to mental health care and motivated by building AI-enhanced experiences that support therapists and patients.

Role Details:

Employment Type: Full Time, Exempt
Base Compensation: The base compensation range for this position is:

• Hybrid Commitment: $200,000–$250,000 USD USD Annually

This role can be hybrid (onsite from our NYC, San Francisco, or Seattle hub location three days per week: Tuesday, Wednesday, Thursday) or fully remote. Both arrangements include travel 2–3 times per year (e.g., company and department off-sites).

As a Staff Data Scientist, reporting to our Director of Analytics, you will play a critical strategic role in shaping Flex's future by guiding key analytics projects across our Customer Support, Finance, Go-To-Market, Marketing, Product, and Strategy teams. You will champion truth seeking across the business, working to bring clarity to some of Flex’s most challenging and complex areas. Through your technical expertise and mastery of data-led story telling, you improve our understanding of the business and where we can make improvements. The ideal candidate for this role is intensely curious, holds themselves to a high analytical bar, while simultaneously balancing analytical rigor with speed and ultimate business impact.

We currently have multiple roles open across Flex Analytics and are prioritizing analyst support for Product, Partnerships and GTM domains.

What you’ll do

• Improve our understanding of key Flex domains by creating scalable frameworks that connect dots across the business into easily interpretable outputs
• Proactively and effectively root cause data and performance issues across the business in order to ensure continued strong performance
• Create and improve dashboards, visualizations, and automations that help us scale analytics across Flex
• Work to establish clear causal links between actions and outcomes through experimentation and other relevant methods, where applicable
• Combine data and strategy - being that bridge between what the data is telling us and the implications for strategic priorities within different areas of the business
• Champion best practices, precise and consistent terminology, and robust documentation within Analytics and more broadly across the organization

Key qualifications

• 5+ years of experience using analytical and statistical methods to support and drive critical business outcomes
• Expert communicator who can break down complex technical concepts into simple business vernacular
• Experience across a variety of analytical domains that allow you to bring unique perspectives to solving hard problems
• Demonstrated success driving business strategy and outcomes from an analytics lens
• Advanced experience with SQL, business intelligence tools (e.g., Tableau, Mode, Looker, etc.)
• Experience with ETL processes, especially utilizing dbt and Snowflake
• Track record of using quantitative, analytical, and problem-solving skills to drive decisions
• Biased for action - knows when to not let perfect be the enemy of good enough, while also can effectively highlight the tradeoffs/risks of moving too quickly too soon
• Strong ownership mindset with a deep natural curiosity to dive into the details and understand how systems work

This role is hybrid with on-site expectations of 3 days per week in our New York Headquarters. 
We also have offices in the Bay Area and Salt Lake City

Flex takes a market-based approach to pay, ensuring compensation is commensurate with a candidate's experience and our internal leveling guidelines. For candidates located in our Tier 1 markets (NYC/ SF), the base salary pay range for this role is $155,000—$194,000 USD.

For all other U.S. locations, Flex utilizes a geographic pay differential based on a cost of labor index. If you are located outside of the Tier 1 states listed above, your starting pay will be adjusted to align with the market conditions of your specific geographic zone. Please speak with your recruiter for additional information regarding the specific range for your location

About the role

As a Sr. Staff Data Scientist reporting to our Director of Analytics, you will play a critical strategic role in shaping Flex's future by guiding key analytics projects across our Product teams. You will champion truth seeking across the business, working to bring clarity to some of Flex’s most challenging and complex areas. Through your technical expertise and mastery of data-led story telling, you improve our understanding of the business and where we can make improvements. The ideal candidate for this role is intensely curious, holds themselves to a high analytical bar, while simultaneously balancing analytical rigor with speed and ultimate business impact.

What you’ll do

• Improve our understanding of key Flex domains by creating scalable frameworks that connect dots across the business into easily interpretable outputs
• Proactively and effectively root cause data and performance issues across the business in order to ensure continued strong performance
• Create and improve dashboards, visualizations, and automations that help us scale analytics across Flex
• Work to establish clear causal links between actions and outcomes through experimentation and other relevant methods, where applicable
• Combine data and strategy - being that bridge between what the data is telling us and the implications for strategic priorities within different areas of the business
• Champion best practices, precise and consistent terminology, and robust documentation within Analytics and more broadly across the organization

Key qualifications

• PhD, Masters, or Bachelor’s Degree with a quantitative focus (Economics, Finance, Computer Science, Mathematics, etc.)
• 8+ years of experience using analytical and statistical methods to support and drive critical business outcomes
• Expert communicator who can break down complex technical concepts into simple business vernacular
• Experience across a variety of analytical domains that allow you to bring unique perspectives to solving hard problems
• Demonstrated success driving business strategy and outcomes from an analytics lens
• Advanced experience with SQL, business intelligence tools (e.g., Tableau, Mode, Looker, etc.)
• Experience with ETL processes, especially utilizing dbt and Snowflake
• Track record of using quantitative, analytical, and problem-solving skills to drive decisions
• Biased for action - knows when to not let perfect be the enemy of good enough, while also can effectively highlight the tradeoffs/risks of moving too quickly too soon
• Strong ownership mindset with a deep natural curiosity to dive into the details and understand how systems work

This role is hybrid with on-site expectations of 3 days per week in our New York Headquarters or Hybrid from our San Francisco office. 

Tier 1 markets (NYC/ SF), the base salary pay range for this role is $194,000-240,000 USD

As a Data Scientist, reporting to our Director of Analytics, you will play a critical strategic role in shaping Flex's future by guiding key analytics projects across our Go-To-Market, Marketing, Product, and Strategy teams. You will champion truth seeking across the business, working to bring clarity to some of Flex’s most challenging and complex areas. Through your technical expertise and mastery of data-led story telling, you improve our understanding of the business and where we can make improvements. The ideal candidate for this role is intensely curious, holds themselves to a high analytical bar, while simultaneously balancing analytical rigor with speed and ultimate business impact.

We currently have multiple roles open across Flex Analytics and are prioritizing analyst support for Product, Partnerships and GTM domains.

What you’ll do

• Improve our understanding of key Flex domains by creating scalable frameworks that connect dots across the business into easily interpretable outputs
• Proactively and effectively root cause data and performance issues across the business in order to ensure continued strong performance
• Create and improve dashboards, visualizations, and automations that help us scale analytics across Flex
• Work to establish clear causal links between actions and outcomes through experimentation and other relevant methods, where applicable
• Combine data and strategy - being that bridge between what the data is telling us and the implications for strategic priorities within different areas of the business
• Champion best practices, precise and consistent terminology, and robust documentation within Analytics and more broadly across the organization

Key qualifications

• 3+ years of experience using analytical and statistical methods to support and drive critical business outcomes
• Expert communicator who can break down complex technical concepts into simple business vernacular
• Experience across a variety of analytical domains that allow you to bring unique perspectives to solving hard problems
• Demonstrated success driving business strategy and outcomes from an analytics lens
• Advanced experience with SQL, business intelligence tools (e.g., Tableau, Mode, Looker, etc.)
• Experience with ETL processes, especially utilizing dbt and Snowflake
• Track record of using quantitative, analytical, and problem-solving skills to drive decisions
• Biased for action - knows when to not let perfect be the enemy of good enough, while also can effectively highlight the tradeoffs/risks of moving too quickly too soon
• Strong ownership mindset with a deep natural curiosity to dive into the details and understand how systems work

This role is hybrid with on-site expectations of 3 days per week in our New York Headquarters. 
We also have offices in the Bay Area and Salt Lake City

For candidates located in our Tier 1 markets (NYC/ SF), the base salary pay range for this role is $133,500—$165,000 USD.

For all other U.S. locations, Flex utilizes a geographic pay differential based on a cost of labor index. If you are located outside of the Tier 1 states listed above, your starting pay will be adjusted to align with the market conditions of your specific geographic zone.

What You’ll Do:

CoreWeave is seeking a Sr. Data Scientist to architect our end-to-end forecasting framework. You will be responsible for advancing our forecasting capabilities across multiple time horizons—from day-ahead utilization to 5-year strategic market projections. As a lead in our forecasting processes, you will bridge the gap between technical data science and business operations, synthesizing inputs from Capacity, Sales, Product, and Finance to inform capital expenditure and infrastructure deployment.

About the Role:

In this role, you will design and advance robust, multi-horizon forecasting processes that capture demand signals for GPU, CPU, and Storage assets across both immediate and long-range windows. You will lead the synthesis of diverse inputs including Sales and Product teams, translating market intelligence and product roadmaps into a unified, data-driven planning signal. You will serve as the forecast lead in planning cycles, working closely with Capacity Managers, Sales, Product, Finance, and Data Center Operations to ensure forecast alignment with physical constraints and financial targets. You will also develop sophisticated models to interpret complex utilization data, providing the organization with a deep understanding of infrastructure consumption patterns and emerging trends, while architecting and maintaining production-grade data pipelines that ensure the integrity, reproducibility, and automation of all forecasting outputs.

Who You Are:

• PhD or Master’s degree in Operations Research, Statistics, Economics, or a related quantitative field.
• 6+ years in demand forecasting or capacity planning, with a proven track record of managing both near-term and long-range model horizons.
• Expert proficiency in Python, SQL, or other programming languages for data manipulation and predictive modeling.
• Significant experience in S&OP/IBP or consensus-based planning environments.
• Ability to translate complex mathematical outputs into clear, actionable strategies for non-technical stakeholders.

Preferred:

• Experience in high-complexity, competitive, or low-margin sectors such as advanced manufacturing, semiconductors, energy, logistics, or cloud infrastructure.
• Familiarity with AI infrastructure (GPUs, CPUs) and data center operational constraints.
• Professional experience applying Operations Research and optimization techniques to solve supply-demand imbalances.

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 to build forecasting frameworks that turn complex signals into practical planning decisions.
• You’re curious about how demand patterns, utilization trends, and market intelligence shape infrastructure strategy over time.
• You’re an expert in predictive modeling, consensus planning, and translating quantitative insights into business action.

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 enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities 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 $143,000 to $210,000. 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).

The Mission

As CoreWeave continues to solidify its position as the Essential Cloud for AI, we are seeking a visionary VP of Product, Research Training Infrastructure. This executive leader will own the product strategy and engineering execution for the services that power the most ambitious AI research labs in the world. You will bridge the gap between "the metal" and the researcher, delivering a seamless, high-performance environment where frontier models are born.

The Role: Architect of the AI Factory

You will lead the product strategy of our Research Training Stack, focusing on the specialized orchestration, evaluation, and iteration tools required for massive-scale pre-training and post-training. This is a mission-critical role at the intersection of high-performance computing (HPC) and cloud-native agility.

Core Responsibilities

• Frontier Orchestration: Oversee the evolution of SUNK (Slurm on Kubernetes) to provide researchers with deterministic, bare-metal performance through a cloud-native interface.
• Holistic Training Services: Beyond Slurm, drive the development of next-generation orchestrators and automated training-based evaluation frameworks that ensure model quality throughout the lifecycle.
• Post-Training Excellence: Build the infrastructure required for sophisticated Reinforcement Learning (RL) and RLHF pipelines, enabling labs to refine foundation models with maximum efficiency.
• Customer Advocacy: Act as the primary technical partner for lead researchers at global AI labs, translating their "future-state" requirements into actionable product roadmaps.

Requirements: Deep Research & Infrastructure Mastery

• Proven Leadership: 15+ years of experience in engineering leadership, with at least 5+ years managing large-scale infrastructure at a top-tier research lab or an AI-native cloud provider.
• Domain Expertise: Deep, hands-on knowledge of Slurm, Kubernetes, and the specific networking requirements (InfiniBand/RDMA) for distributed training clusters.
• Research Mindset: You likely come from a background supporting frontier model research (pre-training and post-training) and understand the "pain points" of a research scientist.
• Scaling Experience: A track record of delivering mission-critical services on multi-thousand GPU clusters (H100/Blackwell/Rubin architectures).
• Strategic Vision: Ability to define "what’s next" in the AI stack, from automated RL loops to specialized sandbox environments.

Why CoreWeave?

In 2026, CoreWeave is the foundation of the largest infrastructure buildout in human history. We are building AI Factories, not just data centers.

• Silicon-Up Innovation: Work directly with the latest NVIDIA architectures.
• Impact: You will be the architect of the environment that enables the next new discovery.

Velocity: We move at the speed of the researchers we support, bypassing legacy cloud bottlenecks to deliver raw power.

The base salary range for this role is $233,000 to $341,000. 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.

Data Science is central to Lyft's products and decision-making. As a Data Scientist on the cross-functional team, you will work in a dynamic environment, tackling a variety of problems from shaping critical business decisions to building algorithms that power our products. We seek passionate, driven Data Scientists to address some of the most interesting and impactful problems in ridesharing.

As a Data Scientist specializing in Algorithms, you will develop mathematical models for the platform's core services, addressing diverse problems in optimization, prediction, machine learning, and inference. On the Fulfillment team, you will collaborate with cross-functional teammates and stakeholders to enhance algorithms for matching rideshare supply and demand in real time and develop product offerings to improve the experiences of Lyft Riders and Drivers.

Responsibilities:

• Leverage data and analytic frameworks to direct creations and improvements of algorithms and models underpinning the team’s systems and products
• Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context.
• Perform exploratory data analysis to gain a deeper understanding of the problem
• Construct and fit statistical, machine learning, or optimization models
• Write production modeling code; collaborate with Software Engineers to implement algorithms in production
• Design and implement both simulated and live traffic experiments
• Analyze experimental and observational data; communicate findings; facilitate launch decisions
• Develop measurement methodologies to monitor the health of our products, as well as the impacts on user outcomes and marketplace outcomes
• Drive collaboration and coordination with cross-functional teams

Experience:

• M.S. or Ph.D. in Machine Learning, Statistics, Operations Research, Computer Science, Mathematics, or other quantitative fields
• 2+ years professional experience in a technology company setting
• Proven experience with building and evaluating machine learning models
• Proficiency with Python and working in a production coding environment
• Passion for solving unstructured and non-standard mathematical problems
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Strong oral and written communication skills, and ability to collaborate and communicate with others to solve a problem

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.

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.

Data Science is at the heart of Lyft’s products and decision-making. You will leverage data and rigorous, analytical thinking to shape our products and make business decisions. This will involve identifying and scoping opportunities, shaping team priorities, recommending and implementing technical solutions, designing experiments, and measuring the impact of new features.

As a Data Scientist on the Risk Solutions team, you will drive a mission critical to both the safety of our community and the financial resilience of our platform. You’ll partner cross-functionally with Product, Engineering, Actuarial and Claims teams to transform complex data into actionable strategies. You will serve as an analytical lead for claims and road safety initiatives, designing experiments and predictive models aimed at reducing accident frequency and claim severity to optimize long-term risk outcomes. If you are a problem-solver eager to apply high-level data science to real-world safety and financial challenges, this role offers a direct path to high-impact innovation.

Responsibilities:

• Leverage large-scale data and analytic frameworks to identify high-impact opportunities to reduce the total cost of claims. 
• Partner with product managers, engineers, actuaries, claims, and operators to translate data insights into decisions and action
• Construct and fit statistical, machine learning, or optimization models
• Design and analyze online experiments; communicate results and act on launch decisions
• Develop analytical frameworks to monitor business and product performance
• Establish metrics that measure the health of our products, the business, as well as the driver experience
• Collaborate with the Claims team to identify high-impact opportunities where data-driven interventions can reduce the total cost of claims.

Experience:

• Degree in a quantitative field such as statistics, actuarial science, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience
• 5+ years of industry experience in a data science, analytics, or management consulting role.  
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Ability to manage, influence, negotiate, and inspire others in a fast-moving environment
• Proficiency in SQL and Python
• Experience with insurance claims analytics a plus
• Strong oral and written communication skills, and ability to collaborate with cross-functional partners

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.

The Rider team is the heartbeat of Lyft business. Every feature you build translates to a better experience for millions of riders globally. The Rider New Product team is the engine of innovation for Lyft's business. The Rider New Product science team’s mission is to rigorously quantify the value of innovation by applying advanced causal inference to unlock Lyft’s next generation of growth. You will have the autonomy of a startup lead with the data scale of a global tech giant.

This is a high-impact, highly technical role within a core business line. The ideal candidate brings strong applied causal inference intuition, hands-on experience with advanced measurement techniques, and the ability to write clean, efficient production code. You will play a critical role in shaping the future of the Lyft rideshare experience by rigorously measuring the true incremental impact of new product features, shaping critical business decisions, and bridging the gap between cutting-edge applied science research and production-scale product impact.

Responsibilities

• Own complex, open-ended incrementality measurement problems. Translate ambiguous product launches into concrete causal frameworks and experimental designs.
• Lead high-impact Causal Inference initiatives. Drive innovation by introducing advanced measurement techniques to quantify the incremental impact of new rider features.
• Partner deeply with Product, Engineering, and Finance. Define the technical vision for how Lyft evaluates innovation, ensuring that we move beyond simple correlations to understand the long-term drivers of rider behavior and value.
• Design and build production-grade measurement systems. Develop and deploy robust causal models pipelines that balance high scientific rigor with the practical constraints.
• Establish robust evaluation frameworks. Ensure that the "engine of innovation" is steering the business toward sustainable, incremental growth.
• Build reusable science infrastructure. Create internal libraries and best practices for causal discovery and automated measurement.
• Mentor and guide junior/mid-level scientists. Serve as a technical advisor on experimental design, statistical modeling, and fostering a culture of scientific excellence.

Experience:

• Advanced Quantitative Background: Master’s or PhD in Economics, Statistics, Applied Math, Computer Science or equivalent high-impact industry experience.
• 3+ Years of Applied Experience: Proven track record in applied science or data science, with a focus on deploying causal models that drive measurable business outcomes.
• Deep product intuition and hands-on experience with causal methods
• Strong proficiency in Python and SQL.
• Experienced in defining and executing sophisticated evaluation strategies, including advanced experiment design and counterfactual analysis to isolate incrementality.
• Proven ability to align cross-functional partners, influence technical architecture, and challenge scientific assumptions to guide high-level product strategy.
• Excellent ability to articulate complex causal concepts, trade-offs between rigor and speed, and scientific findings to both technical peers and executive stakeholders.
• Preferred Qualifications
  • Demonstrated ability to own high-stakes, open-ended problem spaces, translating vague business questions into rigorous scientific roadmaps.
  • Experience in mentoring other scientists, elevating the bar for technical quality, and establishing best practices for modeling and scientific reasoning.

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 $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.

The Lyft Urban Solution team is developing the future of micromobility and is looking for a Data Scientist to inform and drive decision-making that charts the way. From New York’s Citi Bike to San Francisco’s Bay Wheels, our micromobility systems depend on smart data-informed decisions to operate efficiently and at scale. Analyses, insights, and algorithms guide both planning and operations, and we’re looking for passionate, driven Data Scientists to take on some of the most interesting and impactful problems in micromobility. 

The set of problems tackled by the Lyft Urban Solutions Operations Technology Team is incredibly diverse. They cut across optimization, prediction, simulation, inference, transportation, analytics and mapping. We collaborate with and inform a wide range of stakeholders, from executives to hardware specialists to local market operations teams. We're looking for someone who is passionate about solving mathematical problems with data, and is excited about working in a fast-paced, innovative and collegial environment.

Responsibilities:

• Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context
• Perform exploratory data analysis to gain a deeper understanding of the problem
• Develop, calibrate, refine, and leverage numerical models (e.g., statistical, simulation)
• Collaborate with Software Engineers to, refine, monitor and troubleshoot algorithms in production
• Design and implement both simulated and live experiments
• Analyze experimental and observational data; communicate findings; guide feature launch and capital/operational spending decisions

Experience:

• M.S. or Ph.D. in Statistics, Operations Research, Mathematics, Computer Science, Engineering or other quantitative fields or related work experience
• 3+ years professional experience in a technology or transportation/logistics company settingPassion for solving unstructured and non-standard mathematical problems and experience building models (especially statistical and simulation - optimization is a bonus)
• End-to-end experience with data, including SQL querying, aggregation, analysis, and visualization
• Proficiency with Python, ideally including both exploratory analyses/visualizations and building models which inform ongoing decision making
• Ability to collaborate and communicate effectively with others to solve problems and align on decisions

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 $128,000 - $160,000, 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.

Data Science is central to Lyft's products and decision-making. As a Data Scientist on the cross-functional team, you will work in a dynamic environment, tackling a variety of problems from shaping critical business decisions to building algorithms that power our products. We seek passionate, driven Data Scientists to address some of the most interesting and impactful problems in ridesharing.

As a Data Scientist specializing in Algorithms, you will develop mathematical models for the platform's core services, addressing diverse problems in optimization, prediction, machine learning, and inference. On the Fulfillment team, you will collaborate with cross-functional teammates and stakeholders to enhance algorithms for matching rideshare supply and demand in real time and develop product offerings to improve the experiences of Lyft Riders and Drivers.

Responsibilities:

• Leverage data and analytic frameworks to direct creations and improvements of algorithms and models underpinning the team’s systems and products
• Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context.
• Perform exploratory data analysis to gain a deeper understanding of the problem
• Construct and fit statistical, machine learning, or optimization models
• Write production modeling code; collaborate with Software Engineers to implement algorithms in production
• Design and implement both simulated and live traffic experiments
• Analyze experimental and observational data; communicate findings; facilitate launch decisions
• Develop measurement methodologies to monitor the health of our products, as well as the impacts on user outcomes and marketplace outcomes
• Drive collaboration and coordination with cross-functional teams

Experience:

• M.S. or Ph.D. in Machine Learning, Statistics, Operations Research, Computer Science, Mathematics, or other quantitative fields
• 2+ years professional experience in a technology company setting
• Proven experience with building and evaluating machine learning models
• Proficiency with Python and working in a production coding environment
• Passion for solving unstructured and non-standard mathematical problems
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Strong oral and written communication skills, and ability to collaborate and communicate with others to solve a problem

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 Seattle 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.

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.

Data Science is at the heart of Lyft’s products and decision-making. You will leverage data and rigorous, analytical thinking to shape our products and make business decisions. This will involve identifying and scoping opportunities, shaping team priorities, recommending and implementing technical solutions, designing experiments, and measuring the impact of new features.

As a Data Scientist on the Risk Solutions team, you will drive a mission critical to both the safety of our community and the financial resilience of our platform. You’ll partner cross-functionally with Product, Engineering, Actuarial and Claims teams to transform complex data into actionable strategies. You will serve as an analytical lead for claims and road safety initiatives, designing experiments and predictive models aimed at reducing accident frequency and claim severity to optimize long-term risk outcomes. If you are a problem-solver eager to apply high-level data science to real-world safety and financial challenges, this role offers a direct path to high-impact innovation.

Responsibilities:

• Leverage large-scale data and analytic frameworks to identify high-impact opportunities to reduce the total cost of claims. 
• Partner with product managers, engineers, actuaries, claims, and operators to translate data insights into decisions and action
• Construct and fit statistical, machine learning, or optimization models
• Design and analyze online experiments; communicate results and act on launch decisions
• Develop analytical frameworks to monitor business and product performance
• Establish metrics that measure the health of our products, the business, as well as the driver experience
• Collaborate with the Claims team to identify high-impact opportunities where data-driven interventions can reduce the total cost of claims.

Experience:

• Degree in a quantitative field such as statistics, actuarial science, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience
• 5+ years of industry experience in a data science, analytics, or management consulting role.  
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Ability to manage, influence, negotiate, and inspire others in a fast-moving environment
• Proficiency in SQL and Python
• Experience with insurance claims analytics a plus
• Strong oral and written communication skills, and ability to collaborate with cross-functional partners

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.

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.

Data Science is central to Lyft's products and decision-making. As a Data Scientist on the cross-functional team, you will work in a dynamic environment, tackling a variety of problems from shaping critical business decisions to building algorithms that power our products. We seek passionate, driven Data Scientists to address some of the most interesting and impactful problems in ridesharing.

As a Data Scientist specializing in Algorithms, you will develop mathematical models for the platform's core services, addressing diverse problems in optimization, prediction, machine learning, and inference. On the Fulfillment team, you will collaborate with cross-functional teammates and stakeholders to enhance algorithms for matching rideshare supply and demand in real time and develop product offerings to improve the experiences of Lyft Riders and Drivers.

Responsibilities:

• Leverage data and analytic frameworks to direct creations and improvements of algorithms and models underpinning the team’s systems and products
• Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context.
• Perform exploratory data analysis to gain a deeper understanding of the problem
• Construct and fit statistical, machine learning, or optimization models
• Write production modeling code; collaborate with Software Engineers to implement algorithms in production
• Design and implement both simulated and live traffic experiments
• Analyze experimental and observational data; communicate findings; facilitate launch decisions
• Develop measurement methodologies to monitor the health of our products, as well as the impacts on user outcomes and marketplace outcomes
• Drive collaboration and coordination with cross-functional teams

Experience:

• M.S. or Ph.D. in Machine Learning, Statistics, Operations Research, Computer Science, Mathematics, or other quantitative fields
• 2+ years professional experience in a technology company setting
• Proven experience with building and evaluating machine learning models
• Proficiency with Python and working in a production coding environment
• Passion for solving unstructured and non-standard mathematical problems
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Strong oral and written communication skills, and ability to collaborate and communicate with others to solve a problem

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.

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.

The Central Market Management & AI (CMM&AI) team, a key part of the broader Rideshare Experience & Marketplace organization, is essential for maintaining a balanced and efficient marketplace. We do so by developing foundational models, business datasets, and decision-making applications that support a wide range of teams across Lyft. These critical platforms and tools power our pricing / pay strategy, operational alignment, and regional strategies, enabling us to compete effectively in the Rideshare landscape.

Data Scientists in CMM&AI solve the foundational problems that drive Lyft’s marketplace. From forecasting supply and demand to optimizing investments and measuring the ROI of growth levers, our work shapes both automated processes and high-level strategic decisions. Because our challenges are unique to a real-time marketplace, we avoid off-the-shelf solutions in favor of creativity and first-principles mathematical reasoning. We leverage a deep stack of technologies across forecasting, machine learning, inference, and optimization to deliver measurable impact.

As a Senior Data Scientist on the Foundational Models team in CMM&AI, you will operate at the intersection of Machine Learning, Data Science, and Economics to build scalable optimization and modeling systems that directly impact Lyft’s top and bottom lines. You will be hands-on with formulating optimization problems, building ML models, productionizing pipelines, and integrating their outputs within decision-making frameworks. You will collaborate with Product, Engineers, Data Scientists, and Analysts to help define the roadmap and architecture for our next generation of foundational marketplace models that accelerate iterations and drive business efficiency.

Responsibilities:

• Optimization & Modeling
  • Design, formulate, and solve complex mathematical optimization problems that power Lyft’s marketplace decisions across pricing, pay, incentives, and resource allocation.
  • Build, deploy, and maintain production-grade ML and optimization models; collaborate with Software Engineering to integrate algorithms into live systems and establish robust monitoring for model performance and data health.
  • Own the full model lifecycle—from problem framing and prototyping through experimental validation and production deployment—refusing a “build and forget” mentality.
  • Apply first-principles mathematical reasoning to marketplace challenges, choosing the simplest effective solution and building complexity only when incremental value justifies the technical debt.
• Technical Strategy & Execution
  • Drive large-scale technical projects from initial concept to high-impact execution, ensuring alignment with business priorities and Lyft’s overarching goals.
  • Contribute to and influence the multi-quarter technical roadmap for foundational models, helping shape the vision and architecture for next-generation optimization and forecasting systems.
  • Champion high standards for code quality through well-tested, maintainable code and the development of shared team components and libraries.
  • Infuse AI capabilities into existing workflows and demonstrate agility in adopting emerging AI models and techniques to keep Lyft at the forefront of marketplace optimization.
• Stakeholder Partnership & Influence
  • Partner with Data Scientists, Engineers, Product Managers, and Business Partners across lever teams (Pricing, Pay, Driver Engagement, Rider Engagement) to frame problems mathematically and within the business context.
  • Serve as a subject matter expert on optimization and modeling, providing technical guidance and thought leadership to elevate the team’s capabilities.
  • Foster a data-driven culture by presenting actionable insights and recommendations to senior leadership and cross-functional stakeholders.
  • Influence stakeholder roadmaps and advise cross-functional partners on the long-term trade-offs of different algorithmic approaches.

Experience:

• Required:
  • M.S. in Operations Research, Industrial Engineering, Mathematics, Computer Science, Statistics, Economics, or other quantitative fields.
  • 4+ years of hands-on experience developing and deploying optimization and/or machine learning models in a production environment.
  • Advanced proficiency in Python and SQL, with a focus on writing clean, maintainable, and well-tested production code.
  • End-to-end experience with data, including querying, aggregation, analysis, and visualization.
  • Passion for solving unstructured and non-standard mathematical problems using first-principles reasoning.
  • Excellent communication skills and a track record of working closely with Software Engineers, Analysts, and Business Stakeholders to drive decision-making.
• Preferred:
  • Ph.D. in Operations Research, Industrial Engineering, Mathematics, Computer Science, Statistics, Economics, or other quantitative fields.
  • Experience in pricing optimization, marketplace design, and/or resource allocation in a two-sided marketplace environment.
  • Proven track record of delivering measurable business value through the full lifecycle of model development, including experimental design and causal inference.
  • Deep understanding of how various levers (e.g., pricing, incentives, supply positioning) influence marketplace equilibrium and system-wide dynamics.
  • Experience with productionizing algorithms for real-time or near-real-time decision systems.
  • Experience influencing technical roadmaps and advising cross-functional partners on the long-term trade-offs of different algorithmic approaches.
  • Exposure to modern AI/ML frameworks or integration patterns

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.

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.

The Pricing team is a centerpiece of Lyft’s marketplace, determining prices for all rideshare products and supporting new initiatives. Dynamic Pricing & Offer Selection sits at the heart of Pricing, focused on determining optimal prices and ETAs in real-time and balancing supply and demand for our two-sided marketplace to drive both short-term and long-term conversion and retention.

As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions. You will leverage your skills to build ML and optimization models and productionalize pipelines that can scale to millions of calls per day while solving critical business problems that have a big impact on the marketplace and rider experience. You will get exposure to a diverse set of real-world problems across optimization, prediction, machine learning, and inference and collaborate closely with teammates and stakeholders across Pricing, from Product Managers to Engineers and Analysts. 

We are looking for someone who is excited about working in a fast-paced, innovative, and impactful environment, and is adept at balancing complexity and efficiency to translate real world business problems into reliable solutions, systems and decision frameworks.

Responsibilities

• Partner with Data Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context
• Write production quality code. Design, build and deploy production-grade ML and Optimization models. Able to build custom methods and tooling beyond off-the-shelf libraries.
• Perform data analysis and build proof-of-concepts to explore and propose ML and Optimization solutions to both new and existing problems. 
• Evaluate machine learning systems against business goals. Collaborate with Engineers to implement algorithms in live systems and ensure the robustness of the systems
• Establish metrics and development measurement methodologies to monitor the health of our products, as well as the impacts on user and marketplace outcomes
• Drive collaboration and coordination with cross-functional teams

Experience

• M.S. or Ph.D. in Machine Learning, Operations Research, Statistics, Computer Science or other quantitative fields
• 2+ years of algorithms experience in a technology company setting
• Proficiency with Python and working in a production coding environment
• Passion for solving unstructured and non-standard mathematical problems and building impactful machine learning models leveraging expertise in one or multiple fields.
• Strong understanding of machine learning methodologies, with proven experience with building and evaluating optimization or machine learning models
• Strong verbal and written communication skills with a good track record of collaborating with others to solve a problem

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 $140,800 - $176,000, 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 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.

The Central Market Management & AI (CMM&AI) team, a key part of the broader Rideshare Experience & Marketplace organization, is essential for maintaining a balanced and efficient marketplace. We do so by developing foundational models, business datasets, and decision-making applications that support a wide range of teams across Lyft. These critical platforms and tools power our pricing / pay strategy, operational alignment, and regional strategies, enabling us to compete effectively in the Rideshare landscape.

Data Scientists in CMM&AI solve the foundational problems that drive Lyft’s marketplace. From forecasting supply and demand to optimizing investments and measuring the ROI of growth levers, our work shapes both automated processes and high-level strategic decisions. Because our challenges are unique to a real-time marketplace, we avoid off-the-shelf solutions in favor of creativity and first-principles mathematical reasoning. We leverage a deep stack of technologies across forecasting, machine learning, inference, and optimization to deliver measurable impact.

As a Senior Data Scientist on the Foundational Models team in CMM&AI, you will operate at the intersection of Machine Learning, Data Science, and Economics to build scalable optimization and modeling systems that directly impact Lyft’s top and bottom lines. You will be hands-on with formulating optimization problems, building ML models, productionizing pipelines, and integrating their outputs within decision-making frameworks. You will collaborate with Product, Engineers, Data Scientists, and Analysts to help define the roadmap and architecture for our next generation of foundational marketplace models that accelerate iterations and drive business efficiency.

Responsibilities:

• Optimization & Modeling
  • Design, formulate, and solve complex mathematical optimization problems that power Lyft’s marketplace decisions across pricing, pay, incentives, and resource allocation.
  • Build, deploy, and maintain production-grade ML and optimization models; collaborate with Software Engineering to integrate algorithms into live systems and establish robust monitoring for model performance and data health.
  • Own the full model lifecycle—from problem framing and prototyping through experimental validation and production deployment—refusing a “build and forget” mentality.
  • Apply first-principles mathematical reasoning to marketplace challenges, choosing the simplest effective solution and building complexity only when incremental value justifies the technical debt.
• Technical Strategy & Execution
  • Drive large-scale technical projects from initial concept to high-impact execution, ensuring alignment with business priorities and Lyft’s overarching goals.
  • Contribute to and influence the multi-quarter technical roadmap for foundational models, helping shape the vision and architecture for next-generation optimization and forecasting systems.
  • Champion high standards for code quality through well-tested, maintainable code and the development of shared team components and libraries.
  • Infuse AI capabilities into existing workflows and demonstrate agility in adopting emerging AI models and techniques to keep Lyft at the forefront of marketplace optimization.
• Stakeholder Partnership & Influence
  • Partner with Data Scientists, Engineers, Product Managers, and Business Partners across lever teams (Pricing, Pay, Driver Engagement, Rider Engagement) to frame problems mathematically and within the business context.
  • Serve as a subject matter expert on optimization and modeling, providing technical guidance and thought leadership to elevate the team’s capabilities.
  • Foster a data-driven culture by presenting actionable insights and recommendations to senior leadership and cross-functional stakeholders.
  • Influence stakeholder roadmaps and advise cross-functional partners on the long-term trade-offs of different algorithmic approaches.

Experience:

• Required:
  • M.S. in Operations Research, Industrial Engineering, Mathematics, Computer Science, Statistics, Economics, or other quantitative fields.
  • 4+ years of hands-on experience developing and deploying optimization and/or machine learning models in a production environment.
  • Advanced proficiency in Python and SQL, with a focus on writing clean, maintainable, and well-tested production code.
  • End-to-end experience with data, including querying, aggregation, analysis, and visualization.
  • Passion for solving unstructured and non-standard mathematical problems using first-principles reasoning.
  • Excellent communication skills and a track record of working closely with Software Engineers, Analysts, and Business Stakeholders to drive decision-making.
• Preferred:
  • Ph.D. in Operations Research, Industrial Engineering, Mathematics, Computer Science, Statistics, Economics, or other quantitative fields.
  • Experience in pricing optimization, marketplace design, and/or resource allocation in a two-sided marketplace environment.
  • Proven track record of delivering measurable business value through the full lifecycle of model development, including experimental design and causal inference.
  • Deep understanding of how various levers (e.g., pricing, incentives, supply positioning) influence marketplace equilibrium and system-wide dynamics.
  • Experience with productionizing algorithms for real-time or near-real-time decision systems.
  • Experience influencing technical roadmaps and advising cross-functional partners on the long-term trade-offs of different algorithmic approaches.
  • Exposure to modern AI/ML frameworks or integration patterns

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. 

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

Brokerage products are the bread and butter of Robinhood. The Brokerage team is fast-paced, dynamic, and deeply collaborative, driving innovation across our suite of brokerage products.

As a Senior Data Scientist on the Data Science team, you'll be part of a high-impact, multi-functional group working closely with partners across product, engineering, user research, design, marketing, finance, and operations. Together, you’ll unlock the power of data to shape the future of our brokerage business and deliver outstanding value to our customers.

We’re looking for a dedicated and ambitious data science leader to help us accelerate product growth through world-class experimentation, analysis, and data-informed strategy. Your work will directly influence the roadmap, uncover new opportunities, and improve the trading experience for millions of users.

This role is based in our Menlo Park office, with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

What you’ll do

• Perform deep-dive analyses to identify product growth opportunities and drive improvements in core business metrics.
• Design, plan, implement, and analyze A/B experiments, and conduct causal inference analysis to evaluate product changes.
• Define and maintain key product performance metrics; build scalable dashboards to monitor these metrics and advise decision-making.
• Build compelling data visualizations and narratives to effectively communicate insights and recommendations to multi-functional partners, collaborators, and senior leadership.
• Lead and implement large-scale analytics projects to uncover user insights, inform product strategy, and deliver measurable business impact.
• Partner closely with Product, Engineering, Operations, and other multi-functional teams to integrate data-driven decision-making into product and business processes.
• Assist with established methods, standard approaches, and promote a data-informed culture within the organization.

What you bring

• Proven expertise in data science, with a strong background in statistical analysis and machine learning.
• Outstanding problem-solving skills and the ability to think critically and creatively.
• Strong programming skills in Python & SQL, or other relevant languages.
• Experience with data visualization tools and techniques.
• Ability to successfully implement projects and compete in a fast-paced environment.
• PhD or master’s degree in a quantitative field such as mathematics, statistics, engineering or natural sciences and at least 6-10 years of experience developing and deploying predictive models.

What we offer

• Challenging, high-impact work to grow your career
• Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
• Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
• Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.

About the team + role

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

Brokerage products are the bread and butter of Robinhood. The Brokerage team is fast-paced, dynamic, and deeply collaborative, driving innovation across our suite of brokerage products.

As a Senior Data Scientist on the Data Science team, you'll be part of a high-impact, multi-functional group working closely with partners across product, engineering, user research, design, marketing, finance, and operations. Together, you’ll unlock the power of data to shape the future of our brokerage business and deliver outstanding value to our customers.

We’re looking for a dedicated and ambitious data science leader to help us accelerate product growth through world-class experimentation, analysis, and data-informed strategy. Your work will directly influence the roadmap, uncover new opportunities, and improve the trading experience for millions of users.

This role is based in our New York office(s), with in-person attendance expected at least 3 days per week.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

What you’ll do

• Perform deep-dive analyses to identify product growth opportunities and drive improvements in core business metrics.
• Design, plan, implement, and analyze A/B experiments, and conduct causal inference analysis to evaluate product changes.
• Define and maintain key product performance metrics; build scalable dashboards to monitor these metrics and advise decision-making.
• Build compelling data visualizations and narratives to effectively communicate insights and recommendations to multi-functional partners, collaborators, and senior leadership.
• Lead and implement large-scale analytics projects to uncover user insights, inform product strategy, and deliver measurable business impact.
• Partner closely with Product, Engineering, Operations, and other multi-functional teams to integrate data-driven decision-making into product and business processes.
• Assist with established methods, standard approaches, and promote a data-informed culture within the organization.

What you bring

• Proven expertise in data science, with a strong background in statistical analysis and machine learning.
• Outstanding problem-solving skills and the ability to think critically and creatively.
• Strong programming skills in Python & SQL, or other relevant languages.
• Experience with data visualization tools and techniques.
• Ability to successfully implement projects and compete in a fast-paced environment.
• PhD or master’s degree in a quantitative field such as mathematics, statistics, engineering or natural sciences and at least 6-10 years of experience developing and deploying predictive models.

What we offer

• Challenging, high-impact work to grow your career
• Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
• Best in class benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
• Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
• Employer-paid life & disability insurance, fertility benefits, and mental health benefits
• Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
• Exceptional office experience with catered meals, events, and comfortable workspaces.

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products.

Building on our track record of AI-powered solutions (e.g., Bits AI, Bits Evolve, and our time series foundation model), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security.

We are focused on two research areas:

1. World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents.
2. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost.

What You'll Do:

• Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability
• Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure
• Design and build simulated environments and RL training loops for on-policy agent training and evaluation
• Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products
• Stay at the forefront of foundation models, world models, and RL-based agent research
• Contribute to research publications, present at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and help open-source key model artifacts and benchmarks

Who You Are:

• You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, world models, AI agents, reinforcement learning, or multimodal learning (or have equivalent experience)
• You have extensive experience designing and implementing deep learning models and agents, with a strong background in distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and ML libraries (PyTorch)
• You have a track record of impactful publications at top-tier venues (e.g., NeurIPS, ICLR, ICML, TMLR)
• You are familiar with efficient training, post-training, and inference techniques for large foundation models
• You can explain complex models and research findings to both technical and non-technical audiences

Bonus Points (any of the following):

• Experience bridging research and real-world product applications, especially with large foundation models, world models, or RL-trained agents
• Passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
• Experience writing production data pipelines and applications
• Hands-on experience with GPU programming and optimization, including CUDA

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth:

• Competitive global benefits 
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Opportunity to collaborate closely with colleagues across the Datadog offices in New York City and Paris
• Opportunity to attend and present at conferences and meetups
• Intra-departmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

About Datadog: 

Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.

Equal Opportunity at Datadog:

Datadog is an Affirmative Action and Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.

Your Privacy:

Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.

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.