Uber—Staff Scientist, Tech

Job description

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.