Senior Data Scientist, Algorithms - Market Management & AI

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 logic, 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 work 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 Data Scientist, you’ll be hands‑on with building ML models, product ionizing pipelines, and integrating their outputs within decision‑making frameworks. You’ll help develop the vision and roadmaps, lead execution of projects, work closely with partner teams to build and scale our products and systems, and deliver on business goals. You are adept at balancing complexity and efficiency, moving fast with an entrepreneurial mindset, and being proactive to make things happen.

• Partner with Data Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context
• Design, build, and deploy production‑grade ML models; collaborate with Software Engineering to integrate algorithms into live systems and establish robust monitoring for model performance and data health.
• Drive large‑scale technical projects from initial concept to high‑impact execution, ensuring alignment with business priorities.
• Serve as a thought leader and subject matter expert, providing coaching and technical guidance to elevate the team's capabilities.
• Champion high standards for code quality through well‑tested, maintainable code and the development of shared team components/libraries.
• Foster a data‑driven culture by presenting actionable insights and recommendations to senior leadership and cross‑functional stakeholders.

Basic Qualifications:

• M.S. in Operations Research, Mathematics, Computer Science, Statistics, or other quantitative fields.
• 4+ years of hands‑on experience developing and deploying machine learning models in a production environment.
• Strong interest in applying ML, optimization, and forecasting to financial, marketplace, and resource allocation challenges.
• Passion for the full model lifecycle, including performance monitoring, maintenance, and iterative improvement—refusing a "build and forget" mentality.
• Advanced proficiency in Python and SQL, with a focus on writing clean, maintainable, and well‑tested code.
• Passion for solving unstructured and non‑standard mathematical problems using first‑principles reasoning.
• End‑to‑end experience with data, including querying, aggregation, analysis, and visualization.
• Excellent communication skills and a track record of working closely with Software Engineers, Analysts, and Business Stakeholders to drive decision‑making.

Preferred Qualifications:

• PhD in Operations Research, Mathematics, Computer Science, Statistics, or other quantitative fields.
• Proven track record of delivering measurable business value through the full lifecycle of model development, including experimental design and causal inference.
• A demonstrated ability to choose the simplest effective solution—building complex models only when the incremental value justifies the technical debt and maintenance cost.
• Deep understanding of how various levers (e.g., pricing, incentives, supply positioning) influence marketplace…