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
- Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics
- Proven experience in experimental design (e.g., A/B testing) and causal inference
- Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets
- Experience with exploratory data analysis, statistical analysis and testing, and model development
- Deep expertise in areas such as marketplace experimentation, causal inference, ML, or optimization, particularly in the context of multi-sided platforms, incentive systems, or logistics
- Proficiency in SQL
- Familiarity with big data technologies (e.g., Spark, Hive, HDFS)
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., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field
- Minimum 5 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role
- Excellent communication and presentation skills, with the ability to articulate technical concepts to diverse audiences, including senior leadership
- Experience leading technical projects and influencing the scope and direction of research
- Strong business acumen and the ability to shape vague questions into well-defined analytical problems and success metrics
