At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
The Central Market Management & AI (CMM&AI) team, a key part of the broader Rideshare Experience & Marketplace organization, is essential for maintaining a balanced and efficient marketplace. We do so by developing foundational models, business datasets, and decision-making applications that support a wide range of teams across Lyft. These critical platforms and tools power our pricing / pay strategy, operational alignment, and regional strategies, enabling us to compete effectively in the Rideshare landscape.
Data Scientists in CMM&AI solve the foundational problems that drive Lyft’s marketplace. From forecasting supply and demand to optimizing investments and measuring the ROI of growth levers, our work shapes both automated processes and high-level strategic decisions. Because our challenges are unique to a real-time marketplace, we avoid off-the-shelf solutions in favor of creativity and first-principles mathematical reasoning. We leverage a deep stack of technologies across forecasting, machine learning, inference, and optimization to deliver measurable impact.
As a Senior Data Scientist on the Foundational Models team in CMM&AI, you will operate at the intersection of Machine Learning, Data Science, and Economics to build scalable optimization and modeling systems that directly impact Lyft’s top and bottom lines. You will be hands-on with 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 help define the roadmap and architecture for our next generation of foundational marketplace models that accelerate iterations and drive 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
Benefits:
- Great medical, dental, and vision insurance options with additional programs available when enrolled
- Mental health benefits
- Family building benefits
- Child care and pet benefits
- 401(k) plan with company match to help save for your future
- In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
- 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
- Subsidized commuter benefits
- Monthly Lyft credits and complimentary Lyft Pink membership
Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.
Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid
The expected base pay range for this position in the San Francisco area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
