# Senior Data Scientist - Optimization, Central Market Management & AI

**Company:** [Lyft](https://hotfix.jobs/companies/lyft)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $148k – $185k/yr
**Experience:** 4+ years
**Skills:** Python, SQL, Machine Learning, Optimization, Forecasting, Operations Research, Causal Inference, Ai/Ml Frameworks
**Posted:** 2026-03-30

> Senior Data Scientist builds and deploys production ML and optimization models for Lyft's marketplace pricing, incentives, and resource allocation. Requires MS in quantitative field, 4+ years experience, Python/SQL proficiency, and first-principles problem-solving.

## Job Description

## 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

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