The fares platform team powers the fares for all of Uber - that’s all riders, earners and eaters across the Uber and Uber Eats app. With [$190 B ]( Uber-Announces-Results-for-Fourth-Quarte r-and-Full-Year-2025/default.aspx) in gross bookings processed by the fares platform annually, we solve high impact problems which are critical to Uber’s success. As a Scientist on this team, you will leverage experiment design, exploratory data analysis, causal inference and model development to solve a wide variety of problems in domains like pricing, policy design, optimization and defect reduction. - - What the Candidate Will Need / Bonus Points - - - - What the Candidate Will Do - - 1. Refine ambiguous questions and generate data backed hypotheses through a deep understanding of the data, systems, customers and business. 2. Use experiments and causal inference methods to validate the hypothesis and drive business goals critical to Uber’s success. 3. Act as a thought partner with cross-functional teams across various disciplines, including product, engineering, and operations, to drive product strategy. Influence the product roadmap for fares and several cross-organizational teams. 4. Build ML models to solve complex problems and create AI agents to automate data science workflows. 5. Communicate findings clearly to technical and non-technical leadership to influence decisions and product direction. - - Basic Qualifications - - 1. PhD., M. S., or Bachelors degree in Statistics, Economics, Machine Learning, Computer Science, or other quantitative fields. 2. 3 years of industry or academic experience as an Applied or Data Scientist or equivalent (with at least two of those years in industry). 3. Experience with experiment design, exploratory data analysis, causal inference and model development. 4. Proficient in both a data ETL language (e.g. SQL) and a scripting language (e.g. Python, R). - - Preferred Qualifications - - 1. 5 years of industry experience as an Applied or Data Scientist or equivalent. 2. Experience using statistical methodologies in platform, marketplace or consumer domains. 3. Demonstrated ability to leverage data and systems thinking for storytelling and root cause analysis. 4. Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership.