Senior Data Scientist, Marketplace

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.

Data Science is at the heart of Lyft's products and decision-making. As a member of the Science team, you will work in a dynamic environment, where we embrace moving quickly to build the world's best transportation. Data Scientists take on a variety of problems ranging from shaping critical business decisions to building the machine learning models and algorithms that power our internal and external products.

The Forecasting and Real-Time Optimization Platform (FORTOP) team in Lyft's Rideshare Experience & Marketplace (REM) org provides reliable, real-time market supply and demand signals and forecasts that power the systems making critical automated decisions for Lyft's business. These signals feed many of Lyft's most important marketplace products, including Dynamic Pricing, Real-Time Supply Management, Fulfillment, etc. As a Senior Data Scientist on FORTOP, you will improve marketplace efficiency by designing, training, and applying machine learning models that deliver accurate real-time and forecast signals under dynamic conditions. We're looking for a driven Senior Data Scientist who is passionate about solving challenging problems with machine learning, and who is excited to work in a fast-paced, innovative, and cross-functional environment where they will take on some of the most interesting and impactful modeling problems in ridesharing.

• →Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context, and shape roadmaps across cross-functional teams
• →Perform exploratory data analysis to gain a deeper understanding of the problem and the marketplace
• →Develop, fit, and evaluate time series forecasting and machine learning models
• →Write production model code; collaborate with Software Engineers to implement and scale models and algorithms in production, with attention to correctness, efficiency, consistency, and technical debt
• →Design and implement both simulated backtesting and live experiments; analyze experimental and observational data, communicate findings, and facilitate launch decisions
• →Define and uplevel monitoring of model and signal health; build and scale tooling that improves the efficiency of operational tasks\

• M.S. or Ph.D. in Statistics, Mathematics, Economics, Operations Research, Computer Science, or other quantitative fields or related work experience
• 5+ years professional experience in a technology company setting involving a product 
• Proven experience with building and evaluating time series forecasting and machine learning models, ideally in real-time or large-scale production settings
• Strong grasp of core ML fundamentals — feature engineering, model evaluation, and managing the bias-variance tradeoff
• Proficiency with Python and modern ML libraries, and experience working in a production coding environment
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Passion for solving unstructured and non-standard mathematical problems
• Strong written and verbal communication; ability to align stakeholders and influence outcomes through reasoning and data