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.
Responsibilities:- 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
- 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.
Skills Required
- 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 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 including feature engineering, model evaluation, and bias-variance tradeoff management
- 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
- Experience designing and analyzing simulated backtesting and live experiments (A/B testing)
- Strong written and verbal communication; ability to align stakeholders and influence outcomes through data
Lyft Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Lyft and has not been reviewed or approved by Lyft.
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Healthcare Strength — Corporate materials describe comprehensive medical, dental, and vision coverage with added access to One Medical and mental-health support, indicating a solid core health offering. This breadth positions health benefits as a relative strength for full-time employees.
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Parental & Family Support — Company information highlights paid parental leave for new parents, with flexibility in how time can be taken. This signals strong family support within the corporate package.
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Leave & Time Off Breadth — U.S. salaried employees have unlimited paid time off alongside company holidays, and hourly roles receive structured PTO and sick time. These policies point to ample time-off availability compared with many roles.
Lyft Insights
What We Do
Lyft was founded in 2012 by Logan Green and John Zimmer to improve people’s lives with the world’s best transportation, and is available to approximately 95 percent of the United States population as well as select cities in Canada. Lyft is committed to effecting positive change for our cities by offsetting carbon emissions from all rides, and by promoting transportation equity through shared rides, bikeshare systems, electric scooters, and public transit partnerships.









