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Uber has a broad set of levers including pricing, matching, search, and customer support that shape customer experience, marketplace health, long term growth, and profitability. In this role, you will lead the creation and rollout of a company wide efficiency measurement framework to identify and unlock untapped arbitrage opportunities across the business. Uber operates on a massive, interconnected marketplace where levers like pricing, matching, search, and customer support aren't just independent tools—they are the engine of our business. As a Principal Scientist for Lever Efficiency, you aren't here to manage a steady state; you are here to build the unified measurement framework that identifies where our next billion dollars of efficiency will come from. This is a high-stakes, high-ambiguity role. You will be tasked with finding "arbitrage" opportunities - places where our digital decisions and real-world impact are out of sync. This requires more than just technical brilliance in econometrics and causal inference; it requires the grit to challenge existing assumptions and the leadership to align dozens of independent science teams toward a single source of truth. The pace is fast, the systems are complex, and the answers aren't in a textbook. If you thrive on taking "messy" data and turning it into a strategic roadmap that moves the needle for a global business, this is where you’ll grow.
Job Responsibility:
Architect and Lead the creation of a company-wide efficiency measurement framework, defining how we value every lever from rider promotions to support investments
Navigate Ambiguity to identify massive measurement gaps across budgeted and unbudgeted initiatives, proposing and executing experiments to close them
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
Scale through Systems by designing and executing complex experiments to validate and quantify the largest identified opportunities, often working with imperfect information
Communicate with Impact, synthesizing complex causal findings into clear, compelling narratives for senior business leaders and executive audiences to drive investment decisions
Own the Outcome by prioritizing opportunities with the greatest business impact, ensuring we aren't just measuring for the sake of science, but for the sake of the real world
Raise the Bar for technical leadership, acting as a multiplier who identifies opportunities for better performance and reduction of "science debt" across the organization
Requirements:
Industry Experience: 8 or more years of industry experience as an Applied Scientist, Data Scientist, or equivalent, with a track record of solving strategically important problems
Domain Expertise: Deep expertise in long-term value measurement, incrementality, and efficiency analysis within a high-scale environment
Technical Foundations: Mastery of experimental design, causal analysis, statistics, and optimization
Tools: High proficiency in SQL and Python to handle and scale models for large-scale datasets
Strategic Communication: Ability to translate complex technical concepts into actionable business strategy for non-technical stakeholders
Nice to have:
Education: PhD in Economics, Statistics, or a related quantitative field
Experience: 10 or more years of industry experience, specifically building and scaling robust data foundations across large, disparate organizations
Advanced Econometrics: Strong background in structural modeling or advanced econometric techniques to evaluate trade-offs between short-term velocity and long-term sustainability
Leadership through Influence: Proven ability to get alignment and buy-in for multi-org technology or measurement transformations
What we offer:
Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
All full-time employees are eligible to participate in a 401(k) plan