About the RoleThe Driver Pricing team develops the algorithms, signals, and insights that power Uber's real-time driver pricing. We use elements of modeling, causal inference, and optimization to set driver prices that dynamically align both customers' and partners' interests while maximizing the value created by the marketplace.We are looking for experienced candidates with a passion for solving new and difficult problems with data. As a Staff Scientist, you will use strong quantitative skills in the fields of statistics, economics, machine learning, and operations research to improve real-time driver pricing. You will work with product managers and engineers on model development, experiment design, and pricing innovations. What the Candidate Will Do
Build statistical, optimization, and machine learning models for strategic insights, simulations, and deeper understanding of marketplace performance
Leverage large and sophisticated datasets to derive proactive insights that inform strategic decisions and improvements in key revenue-generating optimization engines at Uber
Design and implement pricing experiments and interpret the results to draw detailed and actionable conclusions
Present findings to senior management to advise on business decisions
Work closely with multi-functional leads and partner teams to develop technical vision, new methodological approaches, and drive team direction
Collaborate with product and engineering to drive pricing improvements end-to-end from conceptualization to final product
Basic Qualifications
Ph.D. or M.S. in Statistics, Economics, Mathematics, Operations Research, Machine Learning, or other related quantitative fields
6+ years of industry experience as an Applied Scientist, Data Scientist, or equivalent
Strong programming skills to prototype models and perform statistical analysis in Python (preferably), R, or similar
Proficiency in using Python, SQL, or similar technologies to work efficiently with large data sets
Experience in experimental design and analysis
Knowledge of underlying mathematical foundations of statistics, economics, machine learning, optimization, and analytics
Experience with exploratory data analysis, statistical analysis and testing, and model development
Preferred Qualifications
Experience leading complex technical projects and substantially influencing the scope and output of others
Track record of effectively engaging senior leadership and partner teams to build understanding of and consensus for the viewpoints of the team
Thought leadership to drive multi-functional projects across multiple teams from concept to production
Excellent communication skills to lead initiatives across multiple product areas and communicate findings with senior leadership
Experience of working with large dataset using Spark, Hive, and HDFS
For New York, NY-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 per year. For Seattle, WA-based roles: The base salary range for this role is USD$203,000 per year - USD$225,500 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.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://docs.google.com/forms/d/e/1FAIpQLSdbY9Bv8-lWDMbpidF2GKXsxzNh11wUUVS7fM1znOfEJsVeA/viewform
