About the Role
Scientists at Uber use data to analyze, improve and automate all aspects of Uber's core rideshare and delivery products. You will be joining the Pricing and Incentives team, which owns our automated real-time pricing and incentives algorithms and platform. You will work on analyzing data, creating new metrics, and design experiments which will help design the models and inform the strategies that maintain reliability and improve the efficiency of Uber's Mobility marketplace.
We are looking for experienced candidates with a passion for analyzing data and solving new and difficult problems with a variety of tools. In this role, you will be able to use your strong quantitative skills and creativity in the fields of statistics, economics, and coding to improve the Uber rider experience as well as the overall marketplace performance.
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What the Candidate Will Need / Bonus Points
---- What the Candidate Will Do ----
- Use data to understand product performance and to identify improvement opportunities.
- Analyze data and build dashboards for monitoring of KPIs and market health
- Design and execute product experiments and interpret the results to draw detailed and actionable conclusions.
- Present findings to inform business decisions.
- 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 ----
- Ph.D., M.S., or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, or other quantitative fields.
- 4+ years of industry or Academic Experience as Applied or Data Scientist or equivalent (out of it at least two in industry).
- Experience in experimental design and analysis.
- Experience with exploratory data analysis, statistical analysis and testing, and model development.
- Proficiency in SQL and Python.
---- Preferred Qualifications ----
- Experience in using Python to work efficiently at scale with large data sets.
- Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics.
- Experience in algorithm development and prototyping.
- Experience in pricing optimization.
- Experience in designing large scale experiments in complex environments.
- Well-honed communication and presentation skills.
For New York, NY-based roles: The base salary range for this role is USD$183,000 per year - USD$203,000 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD$183,000 per year - USD$203,000 per year.
For Seattle, WA-based roles: The base salary range for this role is USD$183,000 per year - USD$203,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$183,000 per year - USD$203,000 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.