About the Role
As a Senior Data Scientist on the Foundational Models team within Lyft's Central Market Management & AI (CMM&AI) team, you will operate at the intersection of Machine Learning, Data Science, and Economics. Your primary responsibility will be to build scalable optimization and modeling systems that directly impact Lyft’s top and bottom lines. This hands-on role involves formulating optimization problems, building ML models, productionizing pipelines, and integrating their outputs within decision-making frameworks. You will collaborate with Product, Engineers, Data Scientists, and Analysts to define the roadmap and architecture for the next generation of foundational marketplace models, accelerating iterations and driving business efficiency.
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.