About the Role
The Mobility & Platform Data Science teams use data to improve and automate all aspects of Uber's core rides products, as we drive growth, retention, engagement, and affinity on the Uber platform. This includes providing insight into how pricing and surge is working and offering opportunities for improvement; understanding adoption and engagement with rides and identifying opportunities for product evolution; designing and analyzing experiments to understand the effects of matching changes and/or incentives on rider and driver behaviors, conversion, and engagement; and working with Engineering teams to ensure integrity of our platform, products, and data.
We are looking for experienced scientists who relish the opportunity to develop novel approaches and apply them at Uber's scale. They ideally have a good balance of causal inference, analysis, experimentation, and modeling knowledge, as well as, an ability to use these skills to identify business opportunities and deliver product recommendations.
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---- What the Candidate Will Do ----
- Develop data-driven business insights and work with cross-functional stakeholders to identify opportunities and recommend prioritization of product, growth and optimization initiatives
- Design and analyze experiments, communicating results that draw detailed and actionable conclusions
- Analyze and contribute to development of optimization algos and ML models for use in mobility matching
- Collaborate with cross-functional teams such as product, engineering and operations to drive system development end-to-end from conceptualization to final product
---- Basic Qualifications ----
- Undergraduate and/or graduate degree in Statistics, Economics, Operations Research or other quantitative fields
- Proficient in SQL and advanced experience using Python/R to able to work efficiently at scale with large datasets
- Knowledge of experimental design and analysis (A/B, Switchbacks, Synthetic Control, Diff in Diff etc)
- Experience with exploratory data analysis, statistical analysis and testing and model development
- Good communication skills across technical, non-technical and executive audiences
---- Preferred Qualifications ----
- PhD, or MS degree in Statistics, Economics, Operations Research or other quantitative fields
- Have a growth mindset; love solving ambiguous, ambitious and impactful problems
For New York, NY-based roles: The base salary range for this role is USD$149,000 per year - USD$165,500 per year.
For San Francisco, CA-based roles: The base salary range for this role is USD$149,000 per year - USD$165,500 per year.
For Seattle, WA-based roles: The base salary range for this role is USD$149,000 per year - USD$165,500 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$149,000 per year - USD$165,500 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.