Data Scientist, Algorithms - Pricing

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.

The Marketplace team at Lyft is responsible for accelerating the growth of the business and for delivering our top business/company goals around financial metrics and marketplace performance. The team provides analysis and use cases to support Product teams on building automated tools to manage growth investments.

We are looking for a Data Scientist with chops in Economics, Statistics, Machine Learning or other quantitative fields to join the Pricing vertical of the Marketplace team that is responsible for rideshare pricing. You will influence existing and future roadmaps with your data analysis, modeling and experimentation skills while building domain expertise in state of the art pricing theory. We're looking for a data & process-driven individual who has extraordinary attention to detail and a track record of analytical problem-solving.

Responsibilities:

• Partner with other scientists and colleagues in the pricing team, and with external teams to frame problems mathematically and within the business context

• Perform exploratory data analysis to gain a deeper understanding of problems

• 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; communicate findings; facilitate launch decisions

Experience :

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

• 1-4+ years of professional experience for PhDs or 3-5+ years for Master's in a data scientist role

• 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

• Proficiency in SQL - able to write structured and efficient queries on large data sets

• Ability to collaborate and communicate with others to solve a problem

• Strong oral and written communication skills, and ability to collaborate with cross-functional partners

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.

The expected range of pay for this position in the San Francisco area is $162,000 - $180,000. 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.