Data Scientist, Decisions - Rider

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

　　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 oral and written communication skills, and ability to collaborate with and influence 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.

　　Starting in September 2023, 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 New York City 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.