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$186,031–$246,500.
Based on 240 similar postings.
Employer
About Uber
Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.
The Staff Scientist, Tech - Science joins the team to serve as a technical multiplier solving complex, global-scale puzzles regarding how people and things move. This high-impact leadership role involves translating unstructured problems into rigorous experiments and productionized solutions while setting the science strategy and experimentation standards across multiple teams. The successful candidate will lead the architecture and design of next-generation algorithms, influence executive-level roadmaps, and own the technical trajectory of critical product areas. Key responsibilities include identifying opportunities for performance efficiency and reducing technical debt. Required skills include deep expertise in statistical inference and experimental design, such as A/B testing and switchbacks. Candidates must be proficient in SQL and Python to work with large-scale datasets using distributed tools like Spark, Hive, and Presto.
Translate unstructured business problems into rigorous experiments and productionized solutions.
Lead the architecture and design of next-generation algorithms and coding frameworks.
Set science strategies and experimentation standards across multiple teams.
Identify opportunities to improve performance and reduce technical debt in software and models.
Present complex technical findings and strategic recommendations to executive leadership.
Own the technical trajectory of critical product areas to ensure modular and observable solutions.
What we're looking for
Hold a B.S., M.S., or Ph.D. degree in Statistics, Computer Science, Applied Mathematics, Operations Research, Economics, or other quantitative fields.
If holding an M.S. degree, have a minimum of 6+ years of industry experience.
If holding a B.S. degree, have a minimum of 8+ years of industry experience.
Possess deep expertise in statistical inference and experimental design, such as A/B testing, switchbacks, and cluster-randomized trials.
Demonstrate proficiency in SQL and Python to work with large-scale datasets and distributed tools like Spark, Hive, and Presto.
Have a track record of foreseeing architectural or scientific problems 12+ months out (preferred).
Have experience leading multi-quarter, cross-functional initiatives that moved significant marketplace or financial metrics in production (preferred).
Possess specialized expertise in Causal Inference at scale, Marketplace Optimization, or ML System Design for high-throughput environments (preferred).
Python
SQL
R
Machine Learning
Mathematical Optimization
Statistical Inference
Causal Inference
Econometrics
Operations Research
Data Science
Control Theory
Data Analysis
Logging Infrastructure
Algorithmic Prototyping