The Mobility Matching Science team at Uber is looking to solve the problems of determining which earners to send an offer to and when, to maintain reliability and ensure the trust of riders and earners alike.
Requirements
- Proficient in 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
- Experience in optimization and/or algorithm development
- Experience with dashboard / data visualization tools (i.e Tableau, Mixpanel, Looker or similar)
- Experience working on online experiments
Responsibilities
- 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
Other
- Ph.D., M.S. or Bachelor's degree in Statistics, Economics, Mathemathics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- Minimum 1 year of industry experience as an Applied or Data Scientist or equivalent.
- Ability to work in self-guided manner
- Good communication skills across technical, non-technical and executive audiences
- Have a growth mindset; love solving ambiguous, ambitious and impactful problems
