The Team

The Biohub in New York is an independent nonprofit research institute that brings together three powerhouse universities - Columbia University, The Rockefeller University, and Yale University - into a single collaborative technology and discovery engine. Biohub itself supports some of the brightest, boldest engineers, data scientists, and biomedical researchers to investigate the fundamental mechanisms underlying disease and develop new technologies that will lead to actionable diagnostics and effective therapies. We are guided by our values of scholarly excellence; disruptive innovation; hands-on engineering/hacking/building; partnership and collaboration; open communication and respect; inclusiveness; and opportunity for all.

Our Vision                                                                     

• We pursue large scientific challenges that cannot be pursued in conventional environments
• We enable individual investigators to pursue their riskiest and most innovative ideas
• The technologies developed at Biohub facilitate research by scientists and clinicians at our home institutions and beyond

Diversity of thought, ideas, and perspectives are at the heart of Biohub and enable disruptive innovation and scholarly excellence. We are committed to cultivating an organization where all colleagues feel inspired and know their work makes an important contribution.

The Opportunity

The Biohub is seeking an accomplished Scientist  I to lead innovative investigations into spatial architecture of molecular systems and its role in controlling cell state dynamics. In our newly established Synthetic Spatial Omics Laboratory, we advance next-generation spatial multi-omics platforms for understanding how cells process combinatorial signals, designing synthetic biology toolkits, and capturing cellular temporal histories with spatial resolution.

The successful candidate will lead independent research projects integrating spatial multi-omics, synthetic biology, and systems-level analysis to investigate molecular neighborhood reorganization in response to perturbations, engineer programmable cellular functions, and identify the molecular drivers of cell state transitions. This position offers the opportunity to drive both technology development and fundamental discovery in a highly collaborative, interdisciplinary research environment. 

What You'll Do

• Lead the development and application of imaging-based multi-omics technologies (e.g., seqFISH and related approaches) to map subcellular molecular organization and cell state transitions.
• Design and perform perturbation experiments to decode how molecular neighborhoods respond to combinatorial signals.
• Engineer synthetic biology systems and profile their behavior using spatial multi-omics to understand and program cellular functions.
• Lead analysis of high-dimensional imaging and sequencing datasets using computational and machine learning approaches to identify organizational and regulatory principles. 
• Collaborate closely with researchers across genomics, synthetic biology, machine learning, and related disciplines.
• Contribute to and lead preprints, publications, presentations, and open science practices.

What You'll Bring

Essential - 

• PhD in Systems Biology, Molecular Biology, Immunology, Bioengineering or a closely related discipline.
• 3-4+ years of postdoctoral or equivalent research experience, with a strong record of scientific productivity.
• Demonstrated expertise in imaging-based omics and/or sequencing-based single-cell technologies.
• Expertise in molecular biology techniques, including cell culture, nucleic acid workflows, and cellular functional assays.
• Strong computational skills in Python, R, or comparable languages, with experience analyzing large-scale imaging and sequencing datasets.
• Proven ability to independently conceptualize research directions, design rigorous experiments, troubleshoot complex workflows, and lead projects from conception to publication.
• Collaborative mindset and strong scientific communication skills.

Nice to have - 

• Established track record in developing novel experimental or computational technologies.
• Prior work with imaging-based transcriptomics or genomics technologies.
• Prior work with immune cell types or immune cell engineering.
• Expertise in synthetic biology, CRISPR technologies, or engineered cell systems.
• Experience with temporal genomics, molecular recording, or lineage-tracing approaches.
• Background in regulatory network analysis, signaling dynamics, or models of cellular computation.

Compensation

The New York City, NY base pay range for a new hire in this role is $120,000.00 - $150,000.00. 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. 

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 #LI-Onsite

About the Team

The Discovery team is organized around the north star of building an AI scientist — a system capable of solving the long-horizon reasoning challenges and core capabilities needed to push the scientific frontier. The team trains large-scale models, runs complex multi-week experiments, and builds novel products at the intersection of AI and science.

About the Role

As a Technical Program Manager on the Discovery team, you will own the systems and programs that determine how fast our research moves: compute planning, scientific RL environment health, and the vendor pipelines that supply them, with scope to incubate new programs in domains like bio R&D. Strong candidates should have an ML engineering or research background and have grown into program leadership. You'll need real technical depth: the ability to debug data pipelines, read RL transcripts to spot issues, and make allocation and quality decisions in real time when experimental or production runs hit problems. You'll need organizational effectiveness in equal measure: the ability to navigate a fast-growing organization, quickly identify the critical people and teams across research, infrastructure, product, and data operations, and coordinate across them without losing velocity.

Join us in our mission to build AI systems that push the frontiers of science and benefit humanity.

Responsibilities

• Manage Discovery's compute planning across supervised learning (SL), reinforcement learning (RL), and sandboxing workloads, including forecasting, allocation, prioritization, and efficiency improvements. Partner with central compute planning to ensure Discovery's needs are represented and met.
• Monitor the health of scientific RL environments (quality, reward integrity, failure rates) and drive issues to resolution.
• Expedite the external vendor pipeline for RL environments, including quality review, reward design, and production integration.
• Work with research teams across life sciences, STEM, and other scientific domains to translate research goals into roadmaps that advance AI scientist capabilities.
• Establish processes and frameworks that bring structure to an unstructured research setting without slowing researchers down.
• Collaborate with research leads, infrastructure engineers, and data operations to identify blockers, prioritize competing needs, and make technical trade-off decisions.

You May Be a Good Fit If You:

• Have a background in ML engineering, ML research, or STEM R&D before transitioning to technical program management
• Have deep, hands-on experience with ML training pipelines, RLHF systems, and large-scale data infrastructure in production.
• Have a track record of building execution plans and inventing high-leverage processes that reduce operational overhead and let researchers focus on research.
• Are a fast learner who builds deep contextual understanding in unfamiliar technical domains and can contribute meaningfully to discussions with researchers.
• Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in a fast-moving research setting
• Have excellent stakeholder management and communication skills, with the ability to influence senior technical staff through clarity, competence, and consistent delivery.
• Are excited about the potential for AI to accelerate scientific discovery

Noom is on a mission to help people live better, longer. We’re a consumer-led digital health company, connecting people to content, coaching, community, and clinicians, to build lasting habits and live healthier lives. We’re a high-growth organization powered by science, technology, and world-class talent. When you join Noom, on any team, you’ll see the impact of your work on the world.

Our Clinical Research Team

Noom’s Clinical Research team generates rigorous, fit-for-purpose evidence that advances our mission across our portfolio of health offerings. We design and execute high-quality clinical and real-world research that supports medication-adjacent care and behavior-change interventions, ensuring our programs are backed by credible science and aligned with regulatory, commercial, and product needs. Our work strengthens Noom’s external scientific reputation while driving internal innovation and growth.

About the Role

As we continue to grow, we’re seeking a Clinical Research Scientist to design and execute rigorous clinical and real-world research across Noom’s health programs. This is a senior individual contributor role responsible for flexing across evidence standards—ranging from medication-adjacent clinical research to consumer-facing evidence development—while maintaining methodological rigor and scientific credibility. You will translate complex scientific findings into actionable insights that inform product strategy, claims development, and commercial decision-making.

You Will

• Design and execute clinical trials, pragmatic studies, and real-world evidence (RWE) initiatives across behavioral programs and digital interventions
• Adapt study designs, endpoints, and levels of rigor based on business and regulatory requirements
• Execute research across traditional site-based clinical settings and decentralized/virtual environments
• Develop statistical analysis plans and oversee analytic execution with senior scientific review
• Identify and mitigate methodological risks related to bias, confounding, and interpretability
• Ensure analytic outputs are defensible for peer review, internal decision-making, and appropriate claims development
• Serve as lead or contributing author on peer-reviewed publications and scientific communications
• Collaborate with academic investigators and external partners on study design, execution, and dissemination
• Translate research findings into clear, defensible insights tailored to product, marketing, medical, and legal/compliance stakeholders
• Align research designs with downstream business use cases while upholding scientific integrity
• Represent Noom’s research function in cross-functional and external forums
  
  

About You

If you’re interested in shaping the future of health by generating rigorous, credible evidence that drives trust and growth across innovative health programs, this role may be for you!

You Have

• PhD with advanced training in clinical research methodology, behavioral science, and statistical analysis
• 5–7+ years of related experience designing and executing rigorous clinical studies
• At least 3 years of experience in an academic research setting preferred
• At least 3 years of experience in a corporate or for-profit environment (e.g., digital health, healthcare technology, life sciences) supporting evidence generation and claims development for consumer-facing products preferred 
• Demonstrated understanding of the biological and behavioral mechanisms underlying GLP-1–based therapies
• Hands-on experience executing clinical trials, including traditional site-based and decentralized/virtual designs
• Strong statistical expertise and experience overseeing end-to-end analytic workflows
• Proven ability to connect study design and outcomes to claims development and business decision-making
• A publication track record demonstrating methodological rigor and analytical excellence
• Experience securing federal or peer-reviewed grant funding
• Experience collaborating with academic teams and commercial partners
• Demonstrated success interfacing with product and marketing teams to meet business objectives
• Working knowledge of relevant regulatory and compliance frameworks 

What Makes This Job Amazing

• The opportunity to shape evidence strategy across innovative, high-impact health programs
  A highly visible, senior IC role with meaningful influence on product and scientific strategy
• The chance to publish, collaborate externally, and elevate Noom’s scientific credibility
• The ability to translate rigorous science into real-world health impact at scale

What You'll Earn and Enjoy

• Pay that reflects your impact. Compensation benchmarked to the market and shaped by your impact and value.
• Investment in your future. A 401(k) with company match to help you build long-term financial security.
• Time to truly recharge. Paid holidays, generous time off, Summer Fridays, and a company-wide Summer Break week so the whole team can unplug together.
• Care that covers it all. Access to top-tier medical plans for you and your family, including a premium-free plan option, alongside dental, vision, and life insurance.
• Support for growing families. Paid parental leave to give you the time and flexibility to focus on what matters most.
• And more to help you thrive. Stock awards, performance-based bonus, and exclusive access to Noom’s products, so you can grow with us, win with us, and enjoy what we build.

The base salary range for this position is $145,000-$170,000. Actual compensation is determined by job-related factors, including experience, skills, qualifications, and location. 

This role is eligible for our NYC or Princeton office locations, operating on a hybrid schedule (2 days per week on-site).

More About Noom

Noom is a leading whole-person health platform on a mission to help people live better, longer. We combine personalized medication with the science of behavior change and psychology, to help people build lasting habits and live longer, healthier lives. With nationally-recognized, holistic programs spanning weight, diabetes prevention, GLP-1 support, menopause, longevity, and more, we serve millions through health plans, employers, and direct-to-consumer offerings. As a trailblazer in the world of health and wellness, Noom makes healthy living easy, fun, and deeply rewarding.

We’re more than a health tech company—we’re a movement. This belief extends to our culture: we empower our people to think big, act boldly, and take pride in their contributions to shaping the future of health. With a strong remote culture, as well as offices in NYC and Princeton, we’ve been named one of the Best Places to Work by Inc., Fortune, Glassdoor, and Quartz—including honors for technology and diversity.

Join us in shaping the future of health. Whether you're remote or in-office, you’ll be part of a passionate, mission-driven team working to make the world a healthier place—one habit at a time.

Noom is proud to be an Equal Opportunity Employer, and all applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, caste, national origin, physical or mental disability, protected veteran status, age, or any other characteristic protected by applicable law. Noom is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities.

To help protect against potential hiring scams, please be aware that all email communications from the Noom Talent team and/or hiring managers will come only from an @noom.com email address. Please ignore any messaging or communication from other domains (e.g. @noom.team). Noom will never ask you for personal payment, require you to purchase equipment, conduct an interview process or messaging exchange via Microsoft Teams, or extend a job offer without the completion of a multi-step interview process featuring a combination of phone, Zoom, and in-person interviews, as well as references. If you are unsure about the validity of a Noom job posting on another website, we strongly encourage you to apply directly through our website.

Come Work With Us:

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

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

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

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

Position Summary:

Metropolitan Commercial Bank is seeking a VP-level Applied AI & Machine Learning Scientist to design, build, and validate production-grade AI/ML and Generative AI solutions in a highly regulated banking environment. This role focuses on high-impact use cases—fraud detection, AML alert optimization, AI-assisted credit memo generation for underwriting decision support, contact center AI assistant/copilots, and personalization for treasury/commercial clients—delivered with rigorous governance, explainability, fairness testing, privacy-by-design, cybersecurity, and model lifecycle controls aligned to SR 11-7 and MCB’s Trustworthy & Responsible AI Principles. The role emphasizes Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

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

Essential Functions & Responsibilities 

Applied AI/ML development:

• Design and implement models for fraud detection, AML alert scoring/triage, AI-generated credit memo drafting and underwriting decision support, contact center AI assistants, and personalization for commercial/treasury use cases.
• Leverage modern methods: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings and vector databases, transformers, boosting, anomaly/outlier detection, and classical ML.
• Embed explainability (e.g., SHAP, interpretable scorecards/monotonic models) and conduct pre-/post-deployment bias testing with documented remediation.

Model validation, documentation & governance (SR 11‑7):

• Produce audit-ready documentation (methodology, assumptions, data lineage, limitations, testing) and register models in the inventory with owners/materiality.
• Facilitate independent validation/effective challenge; obtain required approvals before deployment; maintain change management and periodic review cadence.
• Define monitoring, drift thresholds, retraining triggers, and safe rollback/kill-switch procedures; maintain human-in-the-loop checkpoints for high-impact decisions.

Productionization & MLOps on Snowflake

• Package, deploy, and operate models via CI/CD, containerization, and model registry; instrument KPIs/KRIs and alerting dashboards. Operate models natively on Snowflake using Snowpark Python, UDFs/UDTFs, Tasks/Streams, and secure external access where required.
• Partner with Engineering to integrate models via secure APIs/batch; ensure scalability, resiliency, and observability in cloud/on‑prem (e.g., Snowflake, Azure ML, Databricks).

Regulatory, privacy, and cybersecurity alignment:

• Design for ECOA/Reg B (adverse action specificity), UDAAP, FCRA, GLBA privacy, and NYDFS 23 NYCRR 500 cybersecurity requirements.
• Apply privacy-by-design (data minimization, purpose limitation, retention), strong access controls/segregation, and secure SDLC/red teaming for GenAI stacks.

Third‑party AI & data stewardship:

• Support due diligence, testing, and ongoing monitoring of vendor AI/data providers per SR 23‑4; evaluate conceptual soundness, fairness, and security.
• Negotiate/verify contractual controls (no vendor training on MCB/NPI, subprocessors disclosure, audit rights, exit/portability).
• Ensure AEDT compliance (NYC Local Law 144) for any HR-related AI tools.

Cross‑functional partnership:

• Collaborate with Model Risk, Compliance/Legal, Cyber/IT, Data Privacy, Internal Audit, and business owners to meet objectives while staying within risk appetite.
• Communicate complex results, risks, and limitations clearly to technical and non‑technical stakeholders (management committees, examiners).

Innovation, coaching, and best practices:

• Evaluate emerging ML/GenAI methods, LLM evaluation techniques, Snowflake‑native capabilities (e.g., vector search, orchestration), and governance tooling; lead POCs within established control gates.
• Mentor junior staff; promote responsible AI practices, documentation standards, and reproducibility.

Qualifications & Skills:

• 6+ years of relevant work experience.
• Expertise in Python (pandas, scikit‑learn), deep learning (PyTorch/TensorFlow), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• MLOps proficiency with CI/CD, containerization (Docker), registries, and observability; cloud ML (Snowflakes-native ML, Azure ML or Databricks preferred).
• Snowflake‑native ML proficiency: Snowpark Python, UDFs/UDTFs, Tasks/Streams; ability to build and operate ML workflows inside Snowflake.
• Data engineering competency (SQL, ETL/pipelines, Spark/PySpark); ability to work with structured/unstructured data.
• Explainability (e.g., SHAP) and fairness testing; ability to produce interpretable reason codes for ECOA/Reg B adverse actions as applicable.
• Strong grasp of SR 11‑7 lifecycle, model documentation, and operational monitoring within three lines of defense governance.
• Excellent communication; ability to translate technical detail to business/risk stakeholders and drive decisions.
• Curiosity and problem‑solving mindset; ability to balance innovation with disciplined risk management.

Preferred Qualifications & Skills

• Master’s or PhD in a relevant field (Computer Science, Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML, NLP, or model interpretability.
• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions; experience with feature management/registry tooling a plus. model registry and pipeline orchestration; Kubernetes a plus.
• RAG architectures, vector databases, prompt engineering, and LLM evaluation (accuracy, hallucination, safety).
• Fairness toolkits and XAI frameworks; experience preparing models for validation, audit, or regulatory exam discussions.
• Familiarity with SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment
• Must have excellent written and verbal communication skills
• Must be a good listener and good teacher
• Demonstrate analytical, troubleshooting and problem-solving skills
• The ability to learn new technologies quickly
• Self-directed individual with technology and communication skills.
• Ability to take in multiple sources of information with an understanding of the bigger picture need, want, and operation of the Bank.
• Collaborative team-player who can find creative and practical solutions in a dynamic work environment.
• Ability to handle ambiguity, juggle multiple matters at once, and quickly and seamlessly shift from one situation or task to another.

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

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

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

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

Scale works with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities.

In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models.

You will:

• Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.
• Design and experiment new approaches to preference optimization.
• Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.
• Publish research findings in top-tier AI conferences.

Ideally you’d have:

• Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.
• Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.
• Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning.
• Excellent written and verbal communication skills

• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer facing role.

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations.

About the ACE team 

The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. 

About This Role

This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.

Ideally you’d have: 

• Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow. You should also be adept at interpreting research literature and quickly turning new ideas into prototypes.
• A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.)
• At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.
• Strong written and verbal communication skills and the ability to operate cross-functionally. 

Nice to have:

• Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax. 
• Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.
• Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.
• Familiarity with agentic reasoning methods such as STaR and PLANSEARCH 
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions.

Scale AI accelerates the development of AI systems by providing the data, infrastructure, and tooling that power the most advanced models in the world. Our teams operate at the intersection of cutting-edge research, large-scale engineering, and real-world deployment, partnering with leading frontier labs, enterprises, and government agencies to push Generative AI into new capabilities and applications.

As AI rapidly evolves from static models to dynamic, agentic systems, Scale is building the foundational research, evaluation methodologies, and agent/RL infrastructure that will define this next era. You’ll join a high-impact research organization driving advances in large-language models, post-training, evaluation, and agentic/RL environments, helping shape how next-generation AI is built, measured, and deployed.

As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

You will:

• Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).  
• Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.
• Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.
• Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.
• Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.
• Stay deeply connected to the research community, understanding major trends, and helping set them.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.
• A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.
• Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.

Scale Labs, Research Scientist — Agent Robustness

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist working on Agent Robustness you will work on the fundamental challenges of building AI agents that are safe and aligned with humans. For example, you might: 

• Research the science of AI agent capabilities with a focus on how they relate to safety, risk factors, and methodologies for benchmarking them;
• Design and build harnesses to test AI agents’ tendency to take harmful actions when pressured to do so by users or tricked into doing so by elements of their environment;
• Design and build exploits and mitigations for new and unique failure modes that arise as AI agents gain affordances like coding, web browsing, and computer use;
• Characterize and design mitigations for potential failure modes or broader risks of systems involving multiple interacting AI agents.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable building and leveraging agent scaffolding, designing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes.
• Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Hands-on experience with agent evaluation frameworks such as SWE-bench, WebArena, OSWorld, Inspect, or similar tools.
• Experience with red-teaming, prompt injection, or adversarial testing of AI systems.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

Scale Labs, Research Scientist — Frontier Risk Evaluations

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist focused on Frontier Risk Evaluations, you will design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems. For example, you might do any or all of the following: 

• Design and build harnesses to test AI models and systems (including agents) for dangerous capabilities such as security vulnerability exploitation, CBRN uplift, and other high-risk activities;
• Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems;
• Publish evaluation methodologies and write technical reports for policymakers.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable building and instrumenting ML pipelines, writing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Experience in crafting evaluations and benchmarks, or a background in data science roles related to LLM technologies.
• Experience with red-teaming or adversarial testing of AI systems.
• Familiarity with AI safety policy frameworks (e.g., NIST AI RMF, EU AI Act, Korea AI Basic Act).

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

As the leading data and evaluation partner for frontier AI companies, Scale is dedicated to advancing the evaluation and benchmarking of large language models (LLMs). We are building industry-leading LLM evals, setting new standards for model performance assessment. Our mission is to develop rigorous, scalable, and fair evaluation methodologies to drive the next generation of AI capabilities.

Our Research teams work with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. As a Staff Machine Learning Research Scientist on the LLM Evals team, you will lead the development of novel evaluation methodologies, metrics, and benchmarks to measure the capabilities and limitations of frontier LLMs. You will help define what "good" looks like in generative AI, driving research that informs both our internal roadmap and the broader research community. This role is critical for designing and executing a roadmap that defines best practices in data driven AI development and will accelerate the next generation of generative AI models in partnership with top foundational model labs.

You will:

• Drive research on the effectiveness and limitations of existing LLM evaluation techniques.
• Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness. 
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects.
• Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols.
• Implement scalable and reproducible evaluation pipelines using modern ML frameworks.
• Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives.
• Mentor and guide research scientists and engineers, providing technical leadership across cross-functional projects.
• Stay deeply engaged with the ML research community, tracking emerging work and contributing to the advancement of LLM evaluation science.
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience tech leading a team of research scientists and research engineers
• Excellent written and verbal communication skills
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer facing role.

About Scale

At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, fueling the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re amplifying access to high-quality data to drive progress toward Artificial General Intelligence (AGI). Building on our history of model evaluation with enterprise and government customers, we are expanding our capabilities to set new standards for both public and private evaluations.

About This Role

This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). The ideal candidate will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale’s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning. Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.

Ideally, you’d have:

• Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow. You should also be skilled at rapidly interpreting research literature and turning new ideas into working prototypes.

• A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.).

• At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning.

• Strong written and verbal communication skills, along with the ability to work effectively across teams.

Nice to have:

• Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX.

• Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents.

• Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar.

• Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH.

• Proficiency in cloud-based ML development, with experience in AWS or GCP environments.

Our research interviews are designed to assess candidates' ability to prototype and debug ML models, their depth of understanding in research concepts, and their alignment with our organizational culture. We do not conduct LeetCode-style problem-solving assessments.

Scale Labs, Research Scientist — AI Controls and Monitoring

As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist focused on AI Controls and Monitoring, you will design methods, systems, and experiments to ensure that advanced AI models and agents remain aligned with intended goals, even in high-stakes or adversarial environments. For example, you might:

• Develop monitoring techniques and observability methods that track AI behavior in real time to identify and flag deviations, emergent capabilities, or anomalous outputs;
• Research mechanisms for layered control, including fail-safes, oversight protocols, and intervention methods that can halt or redirect AI systems when risks are detected;
• Design red-team simulations to probe weaknesses in oversight and control mechanisms, and build mitigations to close identified gaps;
• Collaborate with policymakers, engineers, and other researchers to establish standards and benchmarks for AI monitoring and escalation.

Ideally you’d have:

• Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Practical experience conducting technical research collaboratively. You should be comfortable designing control and monitoring experiments for AI systems, building prototype systems, and quickly turning new ideas from the research literature into working prototypes.
• A track record of published research in machine learning, particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Experience with runtime monitoring, anomaly detection, or observability for ML systems.
• Familiarity with AI control or alignment research (e.g., scalable oversight, interpretability, debate).
• Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches.

Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement.

As the leading data and evaluation partner for frontier AI companies, Scale is dedicated to advancing the evaluation and benchmarking of large language models (LLMs). We are building industry-leading LLM evals, setting new standards for model performance assessment. Our mission is to develop rigorous, scalable, and fair evaluation methodologies to drive the next generation of AI capabilities.

Our Research teams work with the industry’s leading AI labs to provide high quality data and accelerate progress in GenAI research. As the Tech Lead Manager of the LLM Evals Research team, you will lead a talented team of research scientists and research engineers focused on developing and implementing novel evaluation methodologies, metrics, and benchmarks to assess the capabilities and limitations of our cutting-edge LLMs. This role is critical for designing and executing a roadmap that defines best practices in data driven AI development and will accelerate the next generation of generative AI models in partnership with top foundational model labs. 

You will:

• Lead a team of highly effective research scientists and research engineers on LLM evals.
• Conduct research on the effectiveness and limitations of existing LLM evaluation techniques.
• Design and develop novel evaluation benchmarks for large language models, covering areas such as instruction following, factuality, robustness, and fairness. 
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects.
• Collaborate with internal teams and external partners to refine metrics and create standardized evaluation protocols.
• Implement scalable and reproducible evaluation pipelines using modern ML frameworks.
• Publish research findings in top-tier AI conferences and contribute to open-source benchmarking initiatives.
• Remain up-to-date on ongoing research in the team, help work through technical challenges, and be involved in design decisions
• Remain deeply involved in the research community, both understanding trends, and setting them
• Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.

Ideally you'd have:

• 5+ years of hands-on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development
• Experience and track of recording in landing major research impacts in a fast-paced environment
• Experience supporting and leading a team of research scientists and research engineers
• Excellent written and verbal communication skills
• Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals
• Previous experience in a customer facing role.

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

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

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

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

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.

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 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 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.

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 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).

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 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). 

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).

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 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.

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 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.

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 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 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.

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.

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.

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.

Staff Software Engineer - AI Research Infrastructure

P-1215

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 agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. 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 and agents are just as valuable as any other core IP, and that high-quality AI should be available to all.

Job Description

As a Staff Software Engineer, AI Research Infrastructure, you will be developing and running the research stack that powers Databricks AI Research. You will design and build services that schedule, orchestrate, and observe large‑scale training and inference experiment workloads across thousands of GPUs, improve our dev tooling and ensure that researchers can iterate quickly without sacrificing reliability, efficiency, or security.

You’ll partner closely with research scientists, ML engineers, and platform teams to turn experimental workloads into robust, repeatable pipelines, and to push the limits of what our infrastructure can support.

The Impact you will have

As a Staff Software Engineer on the AI Research Infra Team at Databricks, you will: 

• Design and implement infrastructure that supports large‑scale experiments, data processing, and model training (e.g., HPC clusters, GPU fleets, or cloud‑based systems)
• Enable researchers to go from idea to large‑scale experiment in minutes, not days, by building powerful abstractions for job submission, scheduling, and monitoring.
• Create tooling that improves research developer productivity, such as experiment management systems, CI/testing infrastructure for research code, and workflows that reduce iteration time.
• Influence the long‑term roadmap for research computation, shaping how Databricks AI Research train, evaluate, and ship models to customers.
• Serve as a technical mentor and force multiplier for other engineers working on compute, infra, and AI systems.

What We Look for

• BS/MS or PhD in Computer Science or related field
• 5+ years of software engineering experience, including substantial time working on large‑scale distributed systems or infrastructure.
• Have deep experience with building and operating distributed systems, data pipelines, or large‑scale backend services, ideally involving GPUs, clusters, or major cloud providers.
• Are proficient in one or more systems programming languages (e.g., C++, Rust, Go, Java, Scala) and can design, implement, and debug complex services.
• Have built or significantly contributed to cluster schedulers, resource managers, or large‑scale job orchestration systems (e.g., Kubernetes, Slurm, Ray, custom internal systems).
• Understand modern ML training and inference workflows (e.g., distributed training, model parallelism, fine‑tuning, evaluation), even if you’re not primarily a research scientist.
• Can move fast and be pragmatic in getting things done, while caring about operational excellence. Have driven complex systems from prototype to stable, well‑owned services.
• Communicate clearly with both researchers and engineers, and enjoy translating between research needs and infra realities.

Nilo Therapeutics is an early-stage biotech company developing groundbreaking therapies for autoimmune and inflammatory diseases. Our experienced team brings together diverse interdisciplinary expertise to modulate immune responses via activation of neural circuits. United by our passion for discovering and translating novel biology, we are dedicated to advancing human health through transformative science.  

We are backed by The Column Group, Lux, and DCVC Bio.   

Position 

We are seeking a highly motivated Scientist / Senior Scientist to join our Therapeutics Biology team. This role will be part of a team focused on foundational discovery biology, evaluating the therapeutic potential of our neuro-immune platform and validating targets across a broad range of autoimmune diseases.

Ideal candidates will use their scientific acumen and collaborative approach to partner across the interdisciplinary Nilo team on all aspects of the drug discovery process. This is an exciting opportunity to lead innovative research projects and make significant contributions to our mission of advancing therapeutic solutions. 

At Nilo Therapeutics, we are a dynamic start-up at the forefront of biotech innovation, driven to discover novel biology and develop new drugs. We seek team members who are inspired by our mission and thrive in a collaborative, rigorous, and entrepreneurial environment. Ideal candidates are self-motivated, proactive problem solvers, excited to build and grow the company alongside a world-class team. If you are comfortable with managing multiple projects and possess a resourceful, can-do attitude, we would love to hear from you. Join us and contribute to our mission of advancing innovative therapies at the intersection of neuroscience and immunology. 

What you’ll do: 

• Independently design and conduct both in vitro and in vivo models of inflammation
• Develop acute and chronic experiments in rodents to evaluate targets using inflammatory disease models 
• Utilize primary cell culture, multi-color flow cytometry, ELISA, basic molecular biology and immune assays to probe the neuro-immune axis
• Perform H&E / immunofluorescence staining and confocal microscopy
• Stay up to date with emerging scientific literature and contribute to the strategic vision of the company. 
• Independently prepare data and present to the team 
• Work to improve existing animal protocols and implement solutions / troubleshoot as needed

Key Qualifications: 

• Ph.D. in Immunology, Biology, or a related discipline
• 0-4 years of biotech or pharma industry experience
• Strong background in immunological assays, both in vitro, ex vivo, and in vivo
• Extensive hands-on experience working with animal models in rodents
• Hands-on experience with multi-color flow cytometry, confocal microscopy, primary cell culture, ELISA, and basic molecular biology techniques
• Excellent organizational and communication skills, with the ability to manage multiple projects and deadlines.
• Flexibility to learn new methods and skills for changing research priorities 
• Ability to keep detailed and organized records of experimental protocols and results 
• Ability to work independently and collaborate as member of a research team
• Ability to prioritize tasks, coordinate work tasks with others, and meet multiple deadlines 
• Proven ability to work collaboratively in a multidisciplinary team environment and motivated to make significant contributions for the growth of a high-paced start-up.
• A passion for high-quality, translational science and a desire to discover novel biology and new drugs
  
  

We offer a competitive compensation package that includes a range of benefits designed to support the well-being and professional growth of our team members. The base compensation range for this role is $120,000 - $170,000.

Nilo Therapeutics is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment. We are committed to creating a diverse and inclusive environment and encourage individuals from all backgrounds to apply. 

Who we are

About Stripe

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

About the team

Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We’re looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact.  Our work is broad and varied, influencing how our products work (e.g. understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background.

What you’ll do

About the internship experience

Our internship program provides the opportunity to work on meaningful business initiatives that will grow the GDP of the internet. Through the internship, you will work with many systems and technologies, gain experience in working with large datasets and analytical methodologies/tools to help us better understand our users and build better products. 

Each intern has a dedicated mentor, and every intern project is part of the team's roadmap that will directly contribute to Stripe's mission. As you collaborate with industry experts on initiatives that expand global commerce, you will develop a strong first-hand understanding of the role analytics plays in steering business strategy and results.

We're not just focused on your immediate contributions; we're invested in your growth. Stripe sees this internship as an opportunity to grow your technical expertise and facilitate personal development, preparing you for a career in the tech industry.

Responsibilities

You will:

• Partner closely with Data Scientists, Data Analysts, and business partners to drive business impact through rigorous analytical solutions
• Apply machine learning, causal inference, or advanced analytics on large datasets to: i) measure results and outcomes, ii) identify causal impact and attribution, iii) predict the future performance of users or products, to drive business success
• Influence business actions and strategy by developing actionable insights through metrics and dashboards.
• Drive the collection of new data and the refinement of existing data sources.
• Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly
• Present your work to the Data Science team, partner teams, and fellow interns

Who you are

Minimum requirements

We’re looking for someone who has:

• Enrolled in a quantitative PhD program (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in winter 2026 or spring/summer 2027
• Experience with a scientific computing language (such as Python, R, etc) and SQL. We believe new programming languages can be learned if the fundamentals and general knowledge are present!
• Knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation
• Experience communicating and collaborating with multidisciplinary stakeholders in a team environment

Preferred qualifications

You also likely have:

• Experience writing and debugging data pipelines
• Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects
• Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts

About the Opportunity

We’re Cleerly – a healthcare company based in Denver, Colorado and New York, New York that’s revolutionizing how heart disease is diagnosed, treated, and tracked.  We were founded in 2017 by one of the world’s leading cardiologists and are a growing team of world-class engineering, operations, medical affairs, marketing, and sales leaders. We raised $223M in Series C funding in 2022 which will enable rapid growth and continued support of our mission.

Cleerly is creating a new standard of care for heart disease through value-based, AI-driven precision diagnostic solutions with the goal of helping prevent heart attacks. Our technology goes beyond traditional measures of heart disease by enabling comprehensive quantification and characterization of atherosclerosis, or plaque buildup, in each of the heart arteries.  Cleerly’s solutions are supported by more than a decade of performing some of the world’s largest clinical trials to identify important findings beyond symptoms that increase a person’s risk of heart attacks.

About the Opportunity

We are seeking a highly skilled Staff Scientist to lead the design, development, validation of innovative clinical workflow that can directly impact the diagnosis and treatment for heart disease. In this senior-level role, you are responsible for architecting a cutting-edge product that integrates our existing technologies while pushing the boundary of innovation in cardiac vascular imaging. You will also represent the company in strategic research collaborations with leading academic and clinical institutions, driving innovation at the intersection of AI and cardiovascular health.

Key Responsibilities

• Research and develop state-of-the-art AI and non-AI algorithms for coronary CT/fluoroscopy imaging applications.
• Implement efficient and robust image processing pipeline to solve challenging problems in low latency environments.
• Translate product and clinical requirements into algorithmic specifications in collaboration with Product and Clinical teams.
• Partner with Regulatory Affairs to support regulatory submissions (e.g., 510(k), De Novo, PCCP), including generating evidence and documentation.
• Collaborate with engineering teams to deploy AI and non-AI based solutions in clinical practice.
• Communicate findings and strategy to internal stakeholders and leadership through clear technical documentation, presentations, and demos.
• Contribute to intellectual property development through patents and scientific publications.
• Mentor junior scientists and foster a culture of technical excellence and scientific rigor.

Required Qualifications & Skills

• PHD. in Computer Science, Data Science, Biomedical Engineering, Biomedical Imaging, or a related technical field, with 5+ years combined postdoc and/or industry experience; OR an M.S. with 8+ years of relevant experience.
• Applied AI/DL research for medical imaging diagnostics and analysis, classical image processing algorithm, optical flow analysis, 2D&3D rigid/deformable image registration
• Proven track record of successful product delivery in medical imaging domain
• Strong experience in C++ and Python.
• Experience of developing and applying graph processing or GNN(graph neural network)
• Prior experience with ITK, OpenCV, VTK, CMake, Slicer is a plus
• Prior experience with cardiac motion modelling or tracking is a plus
• Excellent interpersonal, cross-functional, and cross-cultural collaboration skills.

Preferred Qualifications

• 5+ years of industry experience in AI-based medical device development or clinical deployment.
• Experience supporting FDA regulatory submissions for ML-based software, including familiarity with De Novo, 510(k), and PCCP pathways.
• Deep domain knowledge in cardiovascular imaging and CT interpretation workflows.

OUR COMPANY IS AN EQUAL OPPORTUNITY EMPLOYER.  We do not discriminate on the basis of race, color, national origin, ancestry, citizenship status, protected veteran status, religion, physical or mental disability, marital status, sex, sexual orientation, gender identity or expression, age, or any other basis protected by law, ordinance, or regulation.

Job duties, activities, and responsibilities are subject to change by our company.

The base salary range for this role varies by location and is aligned to market benchmarks.

• Candidates located in higher-cost labor markets, including California, Washington, New York, New Jersey, Connecticut, Massachusetts, and Washington, DC represent the middle to high end of the range, while candidates located in all other U.S. locations represent the low to middle end of the range.
• Final compensation is determined based on location, experience, skills, and internal equity.

This role is eligible for a 15% target annual bonus, resulting in the following base salary and Total Target Compensation (TTC) ranges:

• Base Salary: $234,000 - $271,000 
• TTC: $268,000 - $311,000

*Total Target Compensation (TTC): Total Cash Compensation (including base pay, variable pay, commission, bonuses, etc.) Additionally, stock options, paid benefits, and employee perks are part of your total rewards.

Official Title: Data Scientist

About Us: YipitData is the leading market research and analytics firm for the disruptive economy and most recently raised $475M from The Carlyle Group at a valuation of over $1B. Every day, our proprietary technology analyzes billions of alternative data points to uncover actionable insights across sectors like software, AI, cloud, e-commerce, ridesharing, and payments.

Our data and research teams transform raw data into strategic intelligence, delivering accurate, timely, and deeply contextualized analysis that our customers—ranging from the world’s top investment funds to Fortune 500 companies—depend on to drive high-stakes decisions. From sourcing and licensing novel datasets to rigorous analysis and expert narrative framing, our teams ensure clients get not just data, but clarity and confidence.

Our award-winning, people-centric culture—recognized by Inc. as a Best Workplace for three consecutive years—emphasizes transparency, ownership, and continuous mastery.

What It’s Like to Work at YipitData:

YipitData isn’t a place for coasting - it’s a launchpad for ambitious, impact-driven professionals. From day one, you’ll take the lead on meaningful work, accelerate your growth, and gain exposure that shapes careers.

Why Top Talent Chooses YipitData:

• Ownership That Matters: You’ll lead high-impact projects with real business outcomes
• Rapid Growth: We compress years of learning into months
• Merit Over Titles: Trust and responsibility are earned through execution, not tenure
• Velocity with Purpose: We move fast, support each other, and aim high—always with purpose and intention

If your ambition is matched by your work ethic - and you're hungry for a place where growth, impact, and ownership are the norm - YipitData might be the opportunity you’ve been waiting for.

About The Role

YipitData turns billions of alternative-data points into signals that institutional investors and Fortune 500 customers depend on for high-stakes decisions. The data science team owns the methods behind those signals. We do causal inference on panel data, predictive modeling against earnings outcomes, and the white papers customers read.

This role pairs applied data science with AI-native tooling. You will own substantive analytical projects end-to-end. You will use LLM coding assistants as a primary collaborator across every phase of the work: exploratory analysis, production code, written deliverables. You will also help shape how the team builds with AI-native workflows, including tool selection, guardrails, and evaluation patterns.

This is a remote-friendly opportunity that can sit in NYC (where our headquarter is located), one of our office hubs (Austin, Miami, Cupertino), or anywhere else in the US. 

As a Data Scientist, you will:

• Translate ambiguous customer questions into well-scoped data science projects spanning panel data, time series, and causal inference
• Engineer features from large alternative-data panels (transaction-level, invoice-level, web-scraped) using Spark 
• Build, validate, and interpret causal and predictive models that link alternative-data signals to financial outcomes such as revenue, earnings surprise, and KPI inflections
• Author technical white papers and customer analyses for institutional investors and Fortune 500 readers, including figures, equations, and narrative framing for sophisticated readers
• Use LLM coding assistants and agents as a primary collaborator: prototype faster, write higher-quality code, audit your own work, and ship deliverables in compressed timelines
• Build internal LLM-driven tooling (agents, eval harnesses, retrieval pipelines) for the broader organization
• Partner with data engineering, product, and revenue teams to close the loop between signal development and the customer-facing product
• Set technical standards for the team: PEP 8, type hints, vectorized operations, reproducible notebooks, sound methodology, and citation discipline

You Are Likely to Succeed If:

• You have shipped multiple data science projects end-to-end and can point to quantifiable customer or business impact
• Your statistical foundations are real, not surface-level. You can defend a causal-inference method choice end-to-end under technical questioning
• You write Python that reads like the work of a senior engineer: clear naming, type hints, vectorized code, no premature abstraction
• You already use LLM coding assistants daily and can describe specific cases where they multiplied your output without compromising quality, and specific cases where you overrode them
• Writing is a primary skill for you, not a side hobby. You communicate as carefully on the page as you do in code
• You take ownership without prompting: you find the right question, ship the answer, and explain it
• Humility about what you do not know matters as much as confidence about what you do
• You read the literature, cite sources, and ground your work in established methods rather than reinventing them

What We Offer:

Our compensation package includes comprehensive benefits, perks, and a competitive salary: 

• We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
• Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
• The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks. 

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

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