Principal Scientist - Lever Efficiency

Uber Uber · Consumer · New York, NY +2 · Data Science

This role focuses on building a company-wide efficiency measurement framework to identify and unlock arbitrage opportunities across Uber's business levers like pricing, matching, search, and customer support. It requires expertise in econometrics, causal inference, experimental design, and large-scale data analysis to drive strategic decisions and business impact.

What you'd actually do

  1. Architect and Lead the creation of a company-wide efficiency measurement framework, defining how we value every lever from rider promotions to support investments.
  2. Navigate Ambiguity to identify massive measurement gaps across budgeted and unbudgeted initiatives, proposing and executing experiments to close them.
  3. Partner and Influence scientists and product leaders across the company, building the data foundations and visualizations required to see the "big picture" of Uber’s efficiency.
  4. Scale through Systems by designing and executing complex experiments to validate and quantify the largest identified opportunities, often working with imperfect information.
  5. Communicate with Impact, synthesizing complex causal findings into clear, compelling narratives for senior business leaders and executive audiences to drive investment decisions.

Skills

Required

  • 8 or more years of industry experience as an Applied Scientist, Data Scientist, or equivalent
  • track record of solving strategically important problems
  • Deep expertise in long-term value measurement, incrementality, and efficiency analysis within a high-scale environment
  • Mastery of experimental design, causal analysis, statistics, and optimization
  • High proficiency in SQL and Python to handle and scale models for large-scale datasets
  • Ability to translate complex technical concepts into actionable business strategy for non-technical stakeholders

Nice to have

  • PhD in Economics, Statistics, or a related quantitative field
  • 10 or more years of industry experience, specifically building and scaling robust data foundations across large, disparate organizations
  • Strong background in structural modeling or advanced econometric techniques to evaluate trade-offs between short-term velocity and long-term sustainability
  • Proven ability to get alignment and buy-in for multi-org technology or measurement transformations

What the JD emphasized

  • company wide efficiency measurement framework
  • arbitrage opportunities
  • massive measurement gaps
  • complex experiments
  • senior business leaders and executive audiences
  • technical leadership
  • science debt