Data Scientist, Algorithms, Integrity & Identity

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.

Data Science is central to Lyfts products and decision-making. As a Data Scientist on the cross-functional team, you will work in a dynamic environment, tackling a variety of problems from shaping critical business decisions to building algorithms that power our products. We seek passionate, driven Data Scientists to address some of the most interesting and impactful problems in ridesharing.

As a Data Scientist specializing in Algorithms, you will develop mathematical models for the platforms core services, addressing diverse problems in optimization, prediction, machine learning, and inference. On the Integrity & Identity team, you will report to a Team Manager overseeing Data Scientists, Data Analysts and Data Engineers. You will collaborate with cross-functional teammates and stakeholders to enhance algorithms for identifying and mitigating different types of Fraud & abuse in real time and develop product offerings to improve the experiences of Lyft Riders and Drivers.

Responsibilities:

Build machine learning models for various fraud & identity related use cases

Partner with Data Scientists, Engineers, Product Managers, and Analysts to frame problems, both mathematically and within the business context.

Perform exploratory data analysis to gain a deeper understanding of the problem

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:

M.S. or Ph.D. in Statistics, Operations Research, Mathematics, Computer Science, or other quantitative fields

2 years professional experience

Experience in solving problems through machine learning in fraud or identity space is highly preferred

Passion for solving unstructured and non-standard mathematical problems

End-to-end experience with data, including querying, aggregation, analysis, and visualization

Proficiency with Python, or another interpreted programming language like R or Matlab

Willingness to collaborate and communicate with others to solve a problem

Benefits:

Great medical, dental, and vision insurance options

Mental health benefits

Family building 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.

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, Thursdays and a team-specific third day. 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 $124,000 – $155,000. 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.