Staff Scientist, Tech

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 cant 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 environmentthis is where youll make your mark.

What youll do

• Translate strategically important, unstructured problems into rigorous experiments and productionized solutions that move the needle for Ubers 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.