Staff Scientist, Tech

Job description

About the role and team Being a Staff Scientist at Uber means solving the complex, global-scale puzzle of how millions of people and things move in the real world. You aren't just an individual contributor; you are a technical multiplier operating at the high-stakes intersection of digital systems and physical movement. Whether you are architecting next-generation marketplace mechanisms or defining the experimentation guardrails for a whole product area, you will be working where performance and scale can’t be separated.

This is a high-impact leadership role that requires navigating extreme ambiguity and making smart decisions with imperfect information. You won't just build models; you will set the scientific strategy for entire organizations, mentoring the next generation of scientists and influencing executive-level roadmaps. If you are a pragmatist who thrives on solving "unsolvable" problems and taking full ownership of outcomes in a fast-paced, high-accountability environment—this is where you’ll make your mark.

What you’ll do Translate strategically important, unstructured problems into rigorous experiments and productionized solutions that move the needle for Uber’s global business. Lead the architecture and design of next-generation algorithms, role-modeling coding best practices and promoting the adoption of key frameworks across the organization. Set the science strategy and experimentation standards across multiple teams, ensuring scientific rigor and driving organization-wide adoption of best practices.

Act as a technical multiplier , identifying opportunities for better performance, efficiency, and reduction of technical debt in software, models, and processes. Influence executive-level roadmaps by presenting complex technical findings and long-term strategic recommendations to senior leadership. Own the technical trajectory of critical product areas, ensuring solutions are designed to be extensible, modular, and observable while balancing short-term needs and long-term productivity.

What you'll need Ph.D., M.S., or B.S. degree in Statistics, Computer Science, Applied Mathematics, Operations Research, Economics, or other quantitative fields. M.S. degree/B.S. degree, along with a minimum of 8+ years of industry experience required. Deep expertise in statistical inference and experimental design (e.g., A/B, switchbacks, cluster-randomized trials). Proficiency in SQL and Python to work efficiently with large-scale datasets and distributed tools (e.g., Spark, Hive, Presto).

Preferred Qualifications

Technical Vision: A track record of foreseeing architectural or scientific problems 12+ months out and addressing them before they impact the business.

Impact & Scale

Experience leading multi-quarter, cross-functional initiatives that moved significant marketplace or financial metrics in production.

Technical Brand

Recognized as a technical leader who can inspire and rally engineers/scientists around a vision while making tough trade-offs with a bias for action.

Domain Mastery

Specialized expertise in Causal Inference at scale, Marketplace Optimization, or ML System Design for high-throughput environments.