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Lyft
Posted 79 days ago
Senior Data Scientist, Decisions - Risk
Brief overview
New York, New York, United States, San Francisco, California, United StatesHybrid
UndergradIn progress or completed
$148,000 - $185,000/yrStated salary range
5+ yearsMinimum professional experience required
370 H-1B visa approvals on recordVerified U.S. Department of Labor sponsorship history
105 green card filings certifiedCompany has a verified history of sponsoring permanent residency
SQL
Python
Statistical modeling
Machine learning
Optimization modeling
Experiment design
Data analysis
Data visualization
Cross-functional collaboration
Analytical thinking

Lyft is a leading ride-hailing service offering car rentals, bike, and scooter-sharing through its mobile app for convenient transportation.

Visa Sponsorship HistoryData powered by U.S. Department of Labor. This does not guarantee sponsorship for this specific role.
370H-1B approved
98%approval rate
105PERM certified
4years sponsoring
57 new H-1B hiresActively sponsoring new employees, not just renewals
Median sponsored wage: $142,560/yrBased on Labor Condition Application filings
Top sponsored rolesSoftware Engineer, Data Scientist, Business Systems Engineer, Engineering Manager, Accountant
Sponsored employees fromChina, India, Canada, France, South korea
Most recent filing: FY2026Latest fiscal year with government sponsorship activity

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. You will leverage data and rigorous, analytical thinking to shape our products and make business decisions. This will involve identifying and scoping opportunities, shaping team priorities, recommending and implementing technical solutions, designing experiments, and measuring the impact of new features.

As a Data Scientist on the Risk Solutions team, you will drive a mission critical to both the safety of our community and the financial resilience of our platform. You’ll partner cross-functionally with Product, Engineering, Actuarial and Claims teams to transform complex data into actionable strategies. You will serve as an analytical lead for claims and road safety initiatives, designing experiments and predictive models aimed at reducing accident frequency and claim severity to optimize long-term risk outcomes. If you are a problem-solver eager to apply high-level data science to real-world safety and financial challenges, this role offers a direct path to high-impact innovation.

Responsibilities:

  • Leverage large-scale data and analytic frameworks to identify high-impact opportunities to reduce the total cost of claims. 
  • Partner with product managers, engineers, actuaries, claims, and operators to translate data insights into decisions and action
  • Construct and fit statistical, machine learning, or optimization models
  • 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, the business, as well as the driver experience
  • Collaborate with the Claims team to identify high-impact opportunities where data-driven interventions can reduce the total cost of claims.

Experience:

  • Degree in a quantitative field such as statistics, actuarial science, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience
  • 5+ years of industry experience in a data science, analytics, or management consulting role.  
  • End-to-end experience with data, including querying, aggregation, analysis, and visualization
  • Ability to manage, influence, negotiate, and inspire others in a fast-moving environment
  • Proficiency in SQL and Python
  • Experience with insurance claims analytics a plus
  • Strong oral and written communication skills, and ability to collaborate with 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 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 New York City 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.