Data Scientist
Lyft
$148,000 - $222,000 / year
Need a reasonable accommodation to apply or interview? Contact us.
JobMinglr uses automated technology to recommend jobs based on profile information and job preferences. Match Score does not determine eligibility for a position, prevent a user from viewing or applying to a job, or make hiring decisions on behalf of an employer.
Description
Job ID: 111268
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.
Responsibilities:
- 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.
- Construct and fit statistical, machine learning, or optimization models.
- Write production modeling code; collaborate with Software Engineers to implement algorithms in production.
- Design and run both simulated and live traffic experiments.
- Analyze experimental and observational data; communicate findings; facilitate launch decisions.
Experience:
Education and Experience:
- Master's degree or foreign equivalent in Business Analytics and Information Management, Data Science, or related field.
- 2 years of experience in job offered, operations research, or related occupation.
Skills:
- Experience creating metrics and analytical tools to track and monitor growth.
- Experience with statistics, machine learning, causal inference, data scientific forecasting, and authoring data pipelines.
- Experience with building and evaluating optimization and inference models, and predictive modeling.
- Experience with writing production quality computer code and using MATLAB, R, and Python.
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 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 - $222,000/year. 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.
#LI-DNI
Benefits
- health insurance
- vision insurance
- 401(k)
- paid time off
- stock options
- parental leave
About this role
Lyft is seeking a Data Scientist to work on problems spanning product, operations, and business strategy in their New York City office. You'd partner with engineers, product managers, and business stakeholders to frame analytical challenges, build statistical and machine learning models, and move them into production. The work involves exploratory analysis, experimentation (both simulated and live), and communicating data-driven insights to inform major decisions.
The role requires a Master's degree in Data Science, Analytics, Business Analytics, or a related field, plus two years of relevant experience. You should be comfortable writing production-quality code in Python, R, or MATLAB; designing metrics and monitoring systems; and working across the full modeling lifecycle—from causal inference and forecasting to optimization and predictive modeling. This is a full-time hybrid position requiring three days per week in the New York office (Mondays, Wednesdays, Thursdays), with flexibility to work remotely up to four weeks annually.
The salary range is $148,000–$222,000 annually, plus potential equity and bonus. Benefits include comprehensive medical, dental, and vision coverage; mental health and family-building support; 18 weeks of paid parental leave; 401(k) matching; and discretionary paid time off for salaried employees.
How this employer is doing
caution
- H1B: Active Visa Sponsor
- News: Layoffs Mentioned
- Legal Issue
- Active Visa Sponsor
Typical pay for this role
$140,000
Typical range: $120,000 to $140,000
In the news
Lyft Sexual Assault Lawsuit Update 2026: Status, Settlements & How to File, Consumer Notice
This role's local market on JobMinglr
Pay for this role
$148,000 to $222,000 per year
That is the employer's posted range, and where you land in it is usually decided in one conversation. Before that conversation, run the numbers: analyze an offer for this role.
Before you apply, worth reading
How JobMinglr reads this job
Every listing here is scored against your profile before you apply: skills overlap, experience level, location and work arrangement, each weighted and explained. You see the score and the reasons, not just a list. How the matching works.