**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 Will 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., M.S. or Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields. - Ability to use Python, SQL, R Spark, or similar technologies to work efficiently with large data sets - Design experiments and interpret the results to draw detailed and actionable conclusions across a variety of key performance indicators ## **Preferred Qualifications** - 4+ years of industry experience as an Applied or Data Scientist or equivalent. - Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams. - Experience leading key technical projects and substantially influencing the scope and output of others. - Experience communicating qualitative research methods and findings to non-qualitative researchers. - Solid theoretical and applied ML skills and a strong background in mathematics and stat - Analyze large data sets to identify behavior trends among good users and bad actors, using statistics, data mining, and machine learning techniques Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing [this form](https://forms.gle/aDWTk9k6xtMU25Y5A). Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.