Senior Data Scientist - Optimization, Central Market Management & AI

Responsibilities

Optimization & Modeling

• Design, formulate, and solve complex mathematical optimization problems that power Lyft’s marketplace decisions across pricing, pay, incentives, and resource allocation.
• Build, deploy, and maintain production-grade ML and optimization models; collaborate with Software Engineering to integrate algorithms into live systems and establish robust monitoring for model performance and data health.
• Own the full model lifecycle—from problem framing and prototyping through experimental validation and production deployment—refusing a “build and forget” mentality.
• Apply first-principles mathematical reasoning to marketplace challenges, choosing the simplest effective solution and building complexity only when incremental value justifies the technical debt.

Technical Strategy & Execution

• Drive large-scale technical projects from initial concept to high-impact execution, ensuring alignment with business priorities and Lyft’s overarching goals.
• Contribute to and influence the multi-quarter technical roadmap for foundational models, helping shape the vision and architecture for next-generation optimization and forecasting systems.
• Champion high standards for code quality through well-tested, maintainable code and the development of shared team components and libraries.
• Infuse AI capabilities into existing workflows and demonstrate agility in adopting emerging AI models and techniques to keep Lyft at the forefront of marketplace optimization.

Stakeholder Partnership & Influence

• Partner with Data Scientists, Engineers, Product Managers, and Business Partners across lever teams (Pricing, Pay, Driver Engagement, Rider Engagement) to frame problems mathematically and within the business context.
• Serve as a subject matter expert on optimization and modeling, providing technical guidance and thought leadership to elevate the team’s capabilities.
• Foster a data-driven culture by presenting actionable insights and recommendations to senior leadership and cross-functional stakeholders.
• Influence stakeholder roadmaps and advise cross-functional partners on the long-term trade-offs of different algorithmic approaches.

Experience

Required

• M.S. in Operations Research, Industrial Engineering, Mathematics, Computer Science, Statistics, Economics, or other quantitative fields.
• 4+ years of hands-on experience developing and deploying optimization and/or machine learning models in a production environment.
• Advanced proficiency in Python and SQL, with a focus on writing clean, maintainable, and well-tested production code.
• End-to-end experience with data, including querying, aggregation, analysis, and visualization.
• Passion for solving unstructured and non-standard mathematical problems using first-principles reasoning.
• Excellent communication skills and a track record of working closely with Software Engineers, Analysts, and Business Stakeholders to drive decision-making.

Preferred

• Ph.D. in Operations Research, Industrial Engineering, Mathematics, Computer Science, Statistics, Economics, or other quantitative fields.
• Experience in pricing optimization, marketplace design, and/or resource allocation in a two-sided marketplace environment.
• Proven track record of delivering measurable business value through the full lifecycle of model development, including experimental design and causal inference.
• Deep understanding of how various levers (e.g., pricing, incentives, supply positioning) influence marketplace equilibrium and system-wide dynamics.
• Experience with productionizing algorithms for real-time or near-real-time decision systems.
• Experience influencing technical roadmaps and advising cross-functional partners on the long-term trade-offs of different algorithmic approaches.
• Exposure to modern AI/ML frameworks or integration patterns