lyft
Senior Data Scientist - Optimization, Central Market Management & AI
At a Glance
- Location
- New York, United States
- Employment
- employment_required
- Experience
- 4+ years
- Compensation
- in the New York City area is $148,000 - $185,000, not inclusive of potential eq
- Posted
- 2026-03-30T18:06:01-04:00
Key Requirements
Required Skills
Domain Knowledge
- Engineering
Benefits & Perks
edical, dental, and vision insurance options with additional programs availa
Requirements
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.
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.
Compensation & 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
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