Principal Scientist - Marketplace

About the Role

The Principal Scientist role supports Uber Marketplace by optimizing the engine that manages Driver Pricing and Incentives. This means building and deploying the economic models that balance driver earnings, marketplace efficiency, and rider affordability in real-time across thousands of cities globally. The work involves deep causal inference, machine learning on massive, real-world datasets, and owning the core principles that define a healthy, liquid marketplace where you move the real world at enormous scale.

Success in this role requires not only generational technical depth but also the ability to drive high-stakes alignment on ambiguous problems with Director and VP-level leaders in Product, Engineering, and Operations. You will navigate constant trade-offs between short-term growth and long-term marketplace sustainability, demanding a deep trip obsessed mindset to simplify immense complexity. If you are ready to set the technical direction for one of Uber's most critical and high-impact product areas, this is where you will grow with the challenge.

• Define the long-term science roadmap for Driver Pricing, matching, and Incentives, translating ambiguous business problems into rigorous, implementable economic and machine learning solutions that drive step-change improvements.
• Design and lead the implementation of complex causal inference and large-scale experimentation (A/B testing) frameworks to measure the true, long-term impact of pricing and incentive policy changes on both driver behavior and overall marketplace health.
• Drive excellence and rigor in science methodologies, leveraging techniques such as machine learning, experimentation, causal inference, time series analysis, and natural language processing to address complex marketplace challenges.
• Build critical, cross-functional alignment with senior Product, Engineering, and Operations leaders, using deep data-driven narratives to advocate for, influence the roadmap of, and secure resources for multi-quarter, multi-team initiatives.
• Act as the organization's technical authority on marketplace dynamics, providing authoritative guidance on model architecture, elevating the science bar across the domain, and mentoring Staff and Senior Data Scientists.
• Unblock critical, large-scale system bottlenecks by investigating and solving data quality, model drift, and real-time performance issues, often requiring deep dives into engineering architecture and production systems.

• 40% Strategic Research & Model Design (Deep work, defining technical direction)
• 30% Cross-functional Leadership & Alignment (Stakeholder reviews, presenting and influencing)
• 20% Mentorship & Technical Review (Design reviews, guiding team members)
• 10% Production Debugging & Incident Response (Unblocking major, high-stakes issues)

• 15+ years of demonstrable experience as an Individual Contributor (IC) in a Data Science, Applied Science, or Quantitative Research role at a high-scale technology company.
• Ph.D. degree in a quantitative field such as Computer Science, Operation Research, Math, Statistics, or Economics; OR 18+ years of relevant experience in lieu of a Ph.D.
• 8+ years of experience directly designing, deploying, and owning real-time machine learning or economic policy systems that govern multi-sided marketplaces or high-volume transactional platforms.
• Demonstrated proficiency in advanced causal inference, large-scale experimentation design (A/B testing), and model development using Python, R, and advanced SQL.

• Proven ability to mentor senior and staff-level Scientists, raising the overall technical capability and systems thinking rigor across multiple teams, even without direct reporting lines.
• Deep practical experience navigating technical debt and legacy systems while driving simultaneous, net-new development in a fast-paced environment, reflecting high adaptability.
• Exceptional track record of influencing executive-level product and business leaders through clear, concise, and non-jargon-heavy storytelling, demonstrating strong person orientation/interpersonal influence.
• Experience with advanced economic modeling, optimization techniques (e.g., matching, routing), or other Operations Research techniques applied to real-world, high-stakes environments.