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About the Role and Team
Working in Science at Uber means solving complex marketplace problems in a high-stakes, fast-moving environment. You will be joining the Rider Pricing and Incentives Science team, which owns our automated real-time pricing systems. You’ll turn massive amounts of unstructured marketplace data into focused algorithmic strategies, navigating ambiguity to build technologies that directly influence every ride decision made across our global mobility marketplace.
In this role, you’ll serve as the vital bridge between technical depth and real-world business impact. We are looking for experienced candidates with strong quantitative expertise across data science, operations research, statistics, and economics. You will thrive here if you possess an owner’s mindset, combine algorithmic rigour with practical execution, and excel at driving complex initiatives through close cross-functional collaboration with product and engineering partners.
What You’ll Do
Build, scale, and optimize statistical, mathematical programming, and ML models for dynamic pricing applications.
Generate data insights to identify opportunities to enhance algorithm performance.
Develop novel experimentation and causal inference methodologies tailored to complex, highly interconnected marketplace settings.
Collaborate deeply across disciplines with Product, Engineering, and Operations to identify growth opportunities, align on key success metrics, and drive systems end-to-end from ideation to production.
Translate granular marketplace data and complex optimization results into clear, actionable business strategies for leadership.
Unblock technical and operational barriers by designing robust diagnostic tools, logging infrastructure, and marketplace metrics.
Basic Qualifications
Ph.D., M.S., or Bachelor's degree in Operations Research, Data Science, Statistics, Economics, Mathematics, Computer Science, or equivalent quantitative field.
5+ years of professional industry experience as an Applied Scientist, Data Scientist, Economist, or equivalent (or 3+ years with a Ph.D.).
Strong foundation in at least one key domain: control theory, mathematical optimization, statistical inference, machine learning, econometrics.
Proficiency in Python/R and SQL for large-scale data analysis and algorithmic prototyping.
Experience in designing, analyzing, and interpreting complex experiments across key marketplace performance indicators.
Preferred Qualifications
6+ years of industry experience applying data science, optimization, or economics in dynamic marketplace or pricing environments.
Demonstrated success partnering closely with Product, Engineering, and Operations partners to deploy algorithms into production.
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