About the Role
The 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 Sr 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.
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What You 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 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., M.S. or Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- Ability to use Python, SQL, R Spark, 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
- 4+ years of industry experience as an Applied or Data Scientist or equivalent.
- 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.
- Experience communicating qualitative research methods and findings to non-qualitative researchers.
- Solid theoretical and applied ML skills and a strong background in mathematics and stat
- Analyze large data sets to identify behavior trends among good users and bad actors, using statistics, data mining, and machine learning techniques
For New York, NY-based roles: The base salary range for this role is USD$174,000 per year - USD$193,500 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD$174,000 per year - USD$193,500 per year.
For Seattle, WA-based roles: The base salary range for this role is USD$174,000 per year - USD$193,500 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.
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.