Staff Scientist, Tech

Uber

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: San Francisco, CASeattle, WANew York, NYSunnyvale, CA
Posted: 49 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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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.

Uber currently has 107 open roles on FindRole.

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At a glance

TL;DR · Staff Scientist, Tech

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The Staff Scientist, Tech - Science joins the Rider Pricing and Incentive team to solve complex marketplace problems involving real-time algorithms for mobility. This role involves defining the scientific strategy and technical roadmap for rider pricing, architecting next-generation pricing algorithms, and establishing experimentation guardrails. You will build modular, extensible models, develop data insights to influence executive roadmaps, and provide technical guidance to junior scientists. The position requires expertise in statistical inference, advanced experimental design like switchbacks and cluster-randomized trials, and quantitative economic modeling. Candidates must be proficient in SQL and Python while working with large-scale distributed data tools such as Spark and Presto. You will address technical debt and optimize system efficiency within high-throughput environments to improve marketplace performance.

Skills

Python SQL Spark Presto Causal Inference Statistical Inference Experimental Design ML System Design Optimization

What you'll do

Define the 1–2 year scientific strategy and technical roadmap for real-time rider pricing.
Design modular and extensible pricing algorithms that are observable at a global scale.
Identify opportunities to improve system efficiency and reduce technical debt across science pipelines.
Translate complex data and technical findings into strategic recommendations for senior leadership.
Direct the evolution of critical pricing systems to balance short-term needs with long-term scalability.
Establish organization-wide experimentation standards and statistical frameworks for causal inference and marketplace testing.
Provide technical guidance to junior scientists to elevate team-wide execution.

What we're looking for

Bachelor's, Master's, or Ph.D. in Economics, Operations Research, Statistics, Computer Science, Applied Mathematics, or a related quantitative field.
Minimum of 6 years of industry experience for M.S./Ph.D. holders or 8 years for B.S. holders building and scaling production quantitative models.
Deep expertise in statistical inference, advanced experimental design, and quantitative economic modeling.
Proficiency in SQL and Python with experience using large-scale distributed data tools like Spark or Presto.
Recognized expertise in marketplace design, pricing algorithms, industrial organization, ML system design, or large-scale optimization (preferred).
Proven track record of anticipating scientific or system bottlenecks 12+ months in advance and building foundational frameworks (preferred).
Demonstrated success leading multi-quarter, org-wide initiatives that moved major marketplace or financial metrics in production (preferred).
Ability to synthesize complex statistical or economic insights into compelling strategic narratives for senior leadership (preferred).

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