At Lyft, our purpose is to serve and connect. To do this, we start with our own community by creating an open, inclusive, and diverse organization.

Data Science is at the heart of Lyft’s products and decision-making. As a member of the Rider 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.

As a Senior Data Scientist in the Rider team, you will leverage data and rigorous, analytical thinking to shape our rider app and make business decisions that put our customers first. You will identify and scope opportunities, shape priorities, recommend technical solutions, design experiments, and measure impact. You will bring a quantitative mindset to decision-making in partnership with product, design, engineering, business, and operations stakeholders throughout the organization. 

You will report to a Data Science Manager in the Rider & Safety Science team.

Responsibilities:

• Leverage data and analytic frameworks to identify opportunities for growth and efficiency.
• Partner with product managers, engineers, marketers, designers, and operators to translate data insights into decisions and action.
• Design and analyze online experiments; communicate results and act on launch decisions.
• Develop analytical frameworks to monitor business and product performance.
• Establish metrics that measure the health of our products, as well as rider and driver experience.

Experience:

• Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience.
• 5+ years of industry experience in a data science or analytics role.
• Proficiency in SQL - able to write structured and efficient queries on large data sets.
• Experience in programming, especially with data science and visualization libraries in Python or R is helpful.
• Experience in online experimentation and statistical analysis.
• Strong communication skills, and ability to collaborate with and influence cross-functional partners.

Benefits:

• 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 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
• 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.

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. 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 New York City area is $148,000 - $185,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.