The Matching team at Uber builds the systems that determine the optimal way to fulfill trips on the Mobility platform. We work on the problems of determining which earners to send an offer to and when. The solutions we build are critical for maintaining reliability and ensuring the trust of riders and earners alike.
Requirements
- 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.
- Proficiency in SQL.
- Experience in algorithm development and prototyping, and with productionizing algorithms for real-time systems.
- Familiarity with big data technologies (e.g., Spark, Hive, HDFS).
- Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
Responsibilities
- Analyze and contribute to development of optimization algos and ML models for use in mobility matching
- Design and analyze experiments, communicating results that draw detailed and actionable conclusions
- Develop data-driven business insights and work with cross-functional stakeholders to identify opportunities and recommend prioritization of product, growth and optimization initiatives
- Collaborate with cross-functional teams such as product, engineering and operations to drive system development end-to-end from conceptualization to final product
- algorithm development and prototyping
- productionizing algorithms for real-time systems
- translate complex analytical results into clear, actionable insights
Other
- Minimum 5 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
- 6+ 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.
