Senior Scientist, Matching

Uber Uber · Consumer · Seattle, WA +2 · Data Science

The role focuses on algorithmic decisions, experimentation, measurement, and data strategy for Uber's global airports, specifically within the Matching group. The primary responsibility is to build systems that determine the optimal way to fulfill trips by deciding which earners to send offers to and when. This involves developing data-driven insights, designing and analyzing experiments, and contributing to the development of optimization algorithms and ML models for mobility matching at Uber's scale.

What you'd actually do

  1. Develop data-driven business insights and work with cross-functional stakeholders to identify opportunities and recommend prioritization of product, growth and optimization initiatives
  2. Design and analyze experiments, communicating results that draw detailed and actionable conclusions
  3. Analyze and contribute to development of optimization algos and ML models for use in mobility matching
  4. Collaborate with cross-functional teams such as product, engineering and operations to drive system development end-to-end from conceptualization to final product

Skills

Required

  • Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
  • Minimum 3 years (with PhD) or 5 years (with Masters degree) of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
  • 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.

Nice to have

  • 6+ years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
  • 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.

What the JD emphasized

  • novel approaches
  • ML models
  • optimization algos
  • marketplace experimentation
  • multi-sided platforms

Other signals

  • algorithmic decisions
  • ML models
  • optimization algos
  • marketplace experimentation
  • multi-sided platforms