Sr Scientist, Tech - Science

The Rider team is the centerpiece of the Uber consumer experience, owning the flagship app used by over 100M monthly active users.

The Person alization team builds the ranking, recommendation and agentic systems that shape what millions of riders see and do in the app. We are looking for a Senior Scientist to bring scientific rigor to this work and to help set its direction. You will be an experimentation and causal inference expert with strong ML fluency who will own how we measure whether our AI/ML systems are working, and you will use deep-dive analysis to show the team where the biggest opportunities are.

You will partner closely with applied scientists and ML engineers to ensure we can trust our results, understand why a model wins or loses, and focus the roadmap on what moves rider outcomes.

• Own experimentation for personalization. Design, run and analyze experiments for ranking, recommendation and agentic features. This includes interleaving, holdouts, switchbacks, and variance reduction (e.g., CUPED/regression adjustment). Use difference-in-differences, synthetic control, instrumental variables, regression discontinuity, or propensity methods, and be clear about the identifying assumptions.

• Close the gap between offline and online evaluation. Build offline evaluation and simulation frameworks (e.g., NDCG/MRR, counterfactual and off-policy evaluation). Diagnose why offline gains don't show up online, including novelty effects, distribution shift and training-serving skew.

• Go deep on the data. Run deep-dive analyses of rider behavior, funnels, and ranking and recommendation outcomes. Include heterogeneous treatment effects and long-term versus short-term tradeoffs. Surface proactive insights that change what the team builds next.

• Define success, guardrail and long-term metrics for personalization surfaces. Make results easy to find and use through dashboards, query templates and debugging tools.

• Shape the roadmap. Turn analysis into clear recommendations for PMs, engineers and applied scientists, and help the team decide which bets to prioritize and which to stop.

• Raise the scientific bar. Review experiment designs and model evaluations, mentor junior scientists, and share best practices across the team. Partner with applied scientists on experiment design for new ML approaches (e.g., listwise ranking, reinforcement learning, evidential deep learning, LLM-based/agentic applications).

• Stay current on advances in causal inference, experimentation and personalization AI/ML.

• M.S. or Bachelor's degree in Statistics, Economics, Computer Science, Mathematics, Operations Research, or another quantitative field.

• 5+ years of industry experience as a Data Scientist, Applied Scientist, Research Scientist, or equivalent, with a track record of owning experimentation and/or causal inference end to end.

• Deep expertise in experiment design and analysis (power, variance reduction, multiple testing, interference/network effects) and in observational causal inference methods.

• Working knowledge of machine learning. You can critique and debug models, understand ranking/recommender approaches and common failure modes, and use ML as an analytical tool.

• Proficiency in Python and SQL, plus PySpark or similar big-data tools. You can build production-quality analysis pipelines.

• Strong business and product sense. You can frame ambiguous problems, take them end to end, and communicate recommendations clearly to technical and non-technical partners

• Ph.D. or M.S. in Statistics, Economics, or a related field with a causal inference or econometrics focus.

• Experience evaluating ranking, recommender, search or feed systems (offline vs. online…