Staff Data Scientist, Algorithms

At Lyft, our mission is to improve people’s lives with the world’s best transportation. To do this, we start with our own community by creating an open, inclusive, and diverse organization.

Lyft connects people to transportation to change the way we live and get around our communities. Lyft is also the country's leader in micromobility.  We own and/or operate bikeshare in New York (Citi Bike), Chicago (Divvy), San Francisco (Bay Wheels), Portland (Biketown), Boston (BlueBikes), DC (Capital Bikeshare), and Minneapolis (Nice Ride) while providing scooter service in major cities across the country.  Our active fleet includes state-of-the-art electric bikes and scooters, services tens of millions of rides per year, and is rapidly growing.

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 algorithms that power our internal and external products. We’re looking for passionate, driven Data Scientists to take on some of the most interesting and impactful problems in ridesharing.

Our business is growing rapidly, and we’re looking for data scientists with the ability and experience to help us scale and improve. As a member of the Operations Technology team, you’ll draw upon rigorous, analytical thinking and advanced systems modeling to help develop the vision for and guide the operation of our bike and scooter-share systems. You will identify and scope opportunities, derive insights, shape priorities, recommend solutions, build models, design experiments, and measure impact. You will leverage a portfolio of modeling techniques to build and ship production models which empower our operating teams to optimize their efficiency and deliver high-quality service to our customers. You will work in partnership with product, business, and operations stakeholders throughout the organization. 

You will report to a Data Science Manager.

• Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context
• Perform exploratory data analysis to gain a deeper understanding of the problem
• Develop and fit statistical, machine learning, or optimization models
• Write production model code; collaborate with Software Engineers to implement algorithms in production
• Design and implement both simulated and live experiments
• Analyze experimental and observational data; develop metrics; communicate findings; facilitate launch decisions and execution

• M.S. or Ph.D. in Statistics, Operations Research, Mathematics, Computer Science, or other quantitative fields or related work experience
• 6+ years professional experience
• Passion for solving unstructured and non-standard, ambiguous mathematical problems, with mathematical optimization experience a plus 
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Proficiency with Python and SQL, and with writing production-level code 
• Strong communicator. Able to coordinate to deliver on complex initiatives
• Strong business sense and understanding of experimentation methodologies

Benefits

• Great medical, dental, and vision insurance options
• Mental health benefits
• In addition to 12 observed holidays, salaried team members have unlimited paid time off, hourly team members have 15 days paid time off
• 401(k) plan to help save for your future
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
• Pre-tax commuter benefits
• Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program

Lyft is an equal opportunity/affirmative action employer committed to an inclusive and diverse workplace. 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 or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.