Staff Scientist, Driver Incentives

About the Role

The Driver Incentives Science team is at the core of ensuring a healthy and efficient supply network (including drivers, AVs, and fleets) on the Uber platform. We design and build the algorithmic and analytical frameworks that power a variety of incentive and positioning products for drivers and other supply types. Our goal is to leverage data to understand driver behavior and marketplace patterns, optimize incentive strategies, and create a more rewarding experience for drivers, ultimately contributing to the overall health and growth of the Uber marketplace.

As a Staff Scientist on this team, you will be a technical leader responsible for developing innovative solutions to complex problems in the driver incentives and broader supply positioning space. You will apply your expertise in economics, statistics, machine learning, and operations research to design, model, and experiment with new incentive structures and supply positioning (drivers, AVs, fleets) methods. You will collaborate closely with product, engineering, and operations teams to bring your insights and algorithms to life, directly impacting millions of drivers and the broader supply ecosystem globally. We are seeking an experienced scientist with a passion for tackling challenging problems, a curiosity for understanding complex systems, and a drive to build impactful solutions.

• Design, build, and analyze statistical, optimization, and machine learning models for a range of applications in incentives and supply positioning, such as personalized offers for drivers, dynamic earning opportunities, and strategic positioning of supply (drivers, AVs, fleets).
• Lead the design, execution, and interpretation of large-scale experiments to test new incentive strategies, supply positioning algorithms, and product features, drawing detailed and actionable conclusions.
• Conduct deep-dive data analyses to understand supply behavior and patterns, identify opportunities for improving incentive effectiveness and supply utilization, and assess the impact of current programs.
• Develop frameworks to optimize driver incentive products by managing trade-offs between driver engagement, incentive spend effectiveness, and overall marketplace efficiency.
• Collaborate with cross-functional teams including product managers, engineers, operations specialists, and other data scientists to define the product roadmap, develop new features, and drive system development from ideation to production.
• Present findings, insights, and recommendations to senior management and business leaders to inform strategic decisions.
• Provide technical mentorship and thought leadership to the team, championing best practices in data science, statistical analysis, and machine learning.
• Stay abreast of the latest advancements in relevant fields and propose new methodologies and approaches to solve key business problems.

• Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
• Minimum 5 years 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.

• 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.