\\\\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 Segmentation Science team is for you! The Matching Segmentation team at Uber builds the systems that determine the optimal way to fulfill trips on the Mobility platform for two of the fastest growing products - Wait and Save and Priority. We work on the problems of determining which earners to send an offer to and when, the price to charge and so on! 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\\\\ \\+ PhD, MS, or Bachelors degree in Operations Research, Statistics, Economics or other quantitative fields \\+ 4+ years of industry experience as Data Scientist or equivalent \\+ Proficient in SQL and advanced experience (2+ years) using Python/R to able to work efficiently at scale with large datasets \\+ Knowledge of experimental design and analysis (A/B, Switchbacks, Synthetic Control, Diff in Diff etc) \\+ Experience with exploratory data analysis, statistical analysis and testing and model development \\+ Good communication skills across technical, non-technical and executive audiences \\\\Preferred Qualifications\\\\ \\+ Ability to work in self-guided manner \\+ Have a growth mindset; love solving ambiguous, ambitious and impactful problems \\+ Experience in optimization and/or algorithm development \\+ Experience with dashboard / data visualization tools (i.e Tableau, Mixpanel, Looker or similar) For San Francisco, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link 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-

Candidates should possess a PhD, MS, or Bachelor's degree in a quantitative field such as Operations Research, Statistics, or Economics. A minimum of 4 years of industry experience as a Data Scientist or in an equivalent role is required. Proficiency in SQL and advanced experience (over 2 years) using Python or R for large datasets is necessary. The role also requires knowledge of experimental design and analysis, including methods like A/B testing, Switchbacks, Synthetic Control, and Diff in Diff. Experience with exploratory data analysis, statistical analysis, testing, and model development is essential. Good communication skills are needed to interact with technical, non-technical, and executive audiences.