**About the Role** Uber Marketplace is at the core of Uber's business, and Delivery Courier Pricing is a strategically critical component of Marketplace. The mission of the team is to foster growth and increase profitability of Uber by pushing the frontiers of machine learning, data science, and economics and developing highly reliable and scalable platforms to accelerate Uber's impact on the transportation industry.This role will drive high-impact projects to optimize pricing at Uber using optimization, machine learning, and causal inference. **What You Will Do** - Develop algorithms for optimizing prices at scale. - Design pricing experiments and use data for model training and pricing decisions. - Use data to understand product performance and to identify improvement opportunities. - Monitor and evaluate the performance of pricing strategies and make adjustments as needed to ensure optimal outcomes. - Collaborate with multi-functional teams across fields such as product, engineering, and operations to drive system development end-to-end from ideation to productization. \-\-\-\- Basic Qualifications ---- - Ph.D. or M.S. degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, or other quantitative fields. -  A minimum of 2+ years of applicable industry experience for candidates without Ph.D. - Ability to use Python, SQL, R 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. - Self-motivated with the ability to work independently and a strong passion to learn and grow \-\-\-\- Preferred Qualifications ---- - Knowledge of the underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics. - Knowledge of experimental design and analysis. - Experience with exploratory data analysis, statistical analysis and testing, and model development. - Ability to use a language like Python to work efficiently at scale with large data sets. - Proficiency in languages like SQL. - Strong communication skills, including through documentation and presentations. - Experience working in a marketplace-related problem space, esp. pricing optimization - Experience designing large-scale price experiments and using the data for pricing decisions. For New York, NY-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$155,000 per year - USD$172,000 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](https://www.uber.com/careers/benefits). Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together. Uber is proud to be an Equal Opportunity 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.