Senior Scientist, MatchingThe Airports science team owns all algorithmic decisions, experimentation, measurement, and data strategy for Uber's global airports. The mandate is to keep the airport marketplace functioning at optimal efficiency, ensuring long-term engagement and growth of riders and earners. Airports are a "mini-Uber" covering Rider Experience, Driver Experience, Matching, Pricing & Incentives, and Forecasting.

The Matching group 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 the Candidate Will 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.

• 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.

Preferred Qualifications

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.

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.

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