> **About the Role** > The focus of the Merchant Pricing team in the delivery marketplace is to ensure the best merchant selection for consumers, innovate on pricing models for merchants to effectively participate in the marketplace to achieve their business objective with highest ROI while aligning the Uber's Delivery business by driving growth and profitability.We are looking for an experienced scientist who relishes the opportunity to develop novel approaches and apply them at Uber’s scale. Specifically, in this role, you will develop solutions to understand the interaction between Customers, Merchants and the Uber platform. > You will be designing and implementing cutting edge models and data solutions will be collaborating with business and engineering teams to solve key challenges facing merchants such as optimizing marketing campaigns, improving merchant values, making the right tradeoff for the business etc. > **What You Will Do** > You will collaborate with other scientists, product managers, and business teams to understand the challenges in our space, then tackle problems that no one else has solved yet. We expect you to deliver end-to-end solutions rather than algorithms, and you will work closely with the engineers on the team to productionize, scale, and deploy your models world-wide. > Basic Qualifications - Ph.D. or M.S. degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, or other quantitative fields. - 2+ years of industry experience as a scientist or equivalent for M.S. degree holders. - 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. > Preferred Qualifications - 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. - Solid Programming skills to prototype models in at least one of Python (preferably), R, Java - Expert in one of the following areas: A/B experimentation design, causal inference, machine learning, economics, or optimization - Experience of working with large dataset using Spark, Hive, HDFS is desired - Ph.D. or M.S. degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, or other quantitative fields. - 2+ years of industry experience as a scientist or equivalent for M.S. degree holders. For San Francisco, CA-based roles: The base salary range for this role is USD$149,000 per year - USD$165,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$149,000 per year - USD$165,500 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 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.