Job Description

About the Role

Have you ever wondered why it’s taking so long for an earner to be matched to your trip, why the ETA is so long, or how an Earner is picked from the many around you? If so, the Mobility Matching Science team is for you!

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.

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.

What You'll 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

Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
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.

Preferred Qualifications

Ph.D. in a relevant quantitative field.
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.
Experience in algorithm development and prototyping, and with productionizing algorithms for real-time systems.
Demonstrated ability to translate complex analytical results into clear, actionable insights and influence product and business strategy.
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.
Familiarity with big data technologies (e.g., Spark, Hive, HDFS).
Strong business acumen and the ability to shape vague questions into well-defined analytical problems and success metrics.