Principal Scientist - Lever Efficiency

Principal Scientist - Lever Efficiency Department: Data Science Team: Data Scientist Location: San Francisco, United States Type: Full-Time **About the Role** 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. **What You’ll Do** **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 des