The mobility and delivery tech (MaDTech) teams are hiring multiple scientists to solve complex, large scale, and ambiguous business problems across different problem spaces. We are responsible for solving all the critical modeling and inference problems related to the core mobility and delivery marketplace and experiences, including matching, pricing & incentives, experimentation, maps, membership and autonomous vehicle integration. **About the Team** The mobility and delivery tech (MaDTech) teams are responsible for the core technologies that support the marketplace and experience for every line of business at Uber. This includes real-time matching, various pricing and incentive levers, experiences for all participants (consumers, earners, merchants etc) on the platform, experimentation platform, maps platform, and membership. The science teams within MaDTechplay a critical role in the end-to-end product development cycle. We are responsible for solving open, ambiguous business problems at global scale from data and modeling perspectives. We generate insights, develop new optimization, ML, and causal inference models, design metrics, run experiments, and interpret the results. We work closely with partners, especially product and engineering teams, to innovate the business models, improve user experiences, and grow the platform. **What You'll Do** + Solve ambiguous, challenging business problems using data-driven approaches + Work closely with multi-functional leads to develop a technical vision and drive team direction + Develop new methodologies for data science including ML and causal inference modeling, coding, analytics, optimization, and experimentation + Present findings to business leaders to inform decisions + Design product experiments and analyze the results to draw detailed and impactful conclusions **Basic Qualifications** + Completing or recently completed a Ph.D. in Operations Research, Machine Learning, Statistics, or other quantitative fields + Knowledge of underlying mathematical foundations of optimization, stochastic processes, statistics, and machine learning + Strong problem solving and analytical abilities + Familiarity with SQL + Proficiency with a programming language such as R or Python **Preferred Qualifications** + Ability to communicate effectively with both technical and business partners + Research mentality with a bias towards action to structure a project from idea to experimentation to prototype to implementation + Independence, excellent communication, and outstanding follow-through - you energetically tackle your work and love the responsibility of being individually empowered + Experience with exploratory data analysis, statistical analysis and model development + Experience in one of the following areas: Operations Research, Revenue Management and Pricing, Advertising, Experimental Design, Assortment Planning, Transportation + Previous industry experience (e.g., internships) For New York, NY-based roles: The base salary range for this role is USD$161,000 per year - USD$179,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$161,000 per year - USD$179,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits. 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://docs.google.com/forms/d/e/1FAIpQLSdb_Y9Bv8-lWDMbpidF2GKXsxzNh11wUUVS7fM1znOfEJsVeA/viewform