Staff Scientist, Mobility Matching

Staff Scientist, Mobility Matching Department: Data Science Team: Data Scientist Location: San Francisco, United States Type: Full-Time **About the Role** Have you ever wondered why it’s taking so long for an earner to be matched to your trip, why the ETA is so long, or how an Earner is picked from the many around you? If so, the Mobility Matching Science team is for you! The Matching team at Uber builds the systems that determine the optimal way to fulfill trips on the Mobility platform. We work on the problems of determining which earners to send an offer to and when. The solutions we build are critical for maintaining reliability and ensuring the trust of riders and earners alike. We are looking for experienced scientists who relish the opportunity to develop novel approaches and apply them at Uber’s scale. They ideally have a good balance of causal inference, analysis, experimentation, and modeling knowledge, as well as, an ability to use these skills to identify business opportunities and deliver product recommendations. **What You'll Do** - Develop data-driven business insights and work with cross-functional stakeholders to identify opportunities and recommend prioritization of product, growth and optimization initiatives - Design and analyze experiments, communicating results that draw detailed and actionable conclusions - Analyze and contribute to development of optimization algos and ML models for use in mobility matching - Collaborate with cross-functional teams such as product, engineering and operations to drive system development end-to-end from conceptualization to final product **Basic Qualifications** - Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field. - Minimum 5 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role. - Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics. - Proven experience in experimental design (e.g., A/B testing) and causal inference. - Proficiency in using Python or R for