Staff Scientist - Ads & Offers

Staff Scientist - Ads & Offers Department: Data Science Team: Data Scientist Location: New York, United States Type: Full-Time ## **About the Role** Every day thousands of merchants use our Advertising and Offers platform to reach users on Uber to grow their businesses. The Science team on Ads & Offers designs and builds the core algorithmic components of this system. As a Staff Scientist on the team, you will work on understanding how various parts of the system (e.g. auction, pacing, bidding, ranking) are performing. You will lead the design and implementation of new algorithms to make our Ads system more efficient and performant. You will also work on the interaction of ads with the different marketing levers available to merchants, like offers. We are looking for experienced candidates, who have had experience building Ads systems to help accelerate our growth. The ideal candidate should possess a strong passion for understanding complex systems, have the curiosity to understand why systems behave in certain ways, have the drive to research / propose new system designs and is a pragmatist. ## **What You'll Do** - Build statistical, optimization, and machine learning models for a range of applications in the Ads & Offer space (e.g. auction, bidding, pacing, ranking). - Design and execute product experiments and interpret the results to draw detailed and actionable conclusions. - Use data to understand product performance and to identify improvement opportunities. - Present findings to senior management to inform business decisions. - Collaborate with cross-functional teams across disciplines such as product, engineering, and marketing to drive system development end-to-end from ideation to productionization. ## **Basic Qualifications** - Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields. - Minimum 4 years of industry experience as an Applied or Data Scientist or equivalent. - Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics. - Experience in experimenta