Staff Scientist, Tech

Staff Scientist, Tech Department: Data Science Team: Data Scientist Location: New York, United States Type: Full-Time **About the Role** Applied Scientists at Uber use data to improve and automate all aspects of Uber's core rideshare and delivery products. You will be joining the Trip Pricing team, which owns our automated real-time pricing algorithms and platform. You will work on designing and implementing pricing models that maintain reliability and improve the efficiency of Uber’s Mobility marketplace. We are looking for experienced candidates with a passion for solving new and difficult problems with data. In this role, you will be able to use your strong quantitative skills in the fields of economics, machine learning, and/or operations research to improve the Uber rider experience as well as the overall marketplace performance. **What the Candidate Will Do** 1. Build statistical, optimization, and machine learning models for a range of applications in the pricing algorithms space. 2. Design and execute product experiments and interpret the results to draw detailed and actionable conclusions. 3. Use data to understand product performance and to identify improvement opportunities. 4. Present findings to senior management to inform business decisions. 5. Collaborate with cross-functional teams across disciplines such as product, engineering, operations, and marketing to drive system development end-to-end from ideation to productionization. **Basic Qualifications** 1. Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields. 2. 3+ years of industry experience as an Applied or Data Scientist or equivalent. 3. Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics. 4. Experience in experimental design and analysis. 5. Experience with exploratory data analysis, statistical analysis and testing, and model development. 6. Ability to use Python to work efficiently at scale with large data sets. 7. Proficiency in SQL. **Preferred Qualifications