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Lyft is hiring a Data Scientist in Seattle to lead causal inference and marketing mix modeling efforts. You will drive data science solutions, handle complex domains, and optimize investments across marketing channels. The role requires advanced statistical skills and proficiency in Python and SQL.
Lyft offers great benefits including medical insurance, parental leave, and 401(k) matching. The expected salary ranges from $136,160 to $170,240 based on experience and qualifications. This is a hybrid position requiring 3 in-office days weekly.
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.
The Growth Products team drives rider and driver acquisition to scale the business and balance the marketplace. We specialize in incentive and messaging targeting, budget optimization, and paid media measurement, and move rapidly to test new ideas and products.
As a Data Scientist expert in causal inference and marketing mix models (MMM), you will lead our efforts to measure and optimize investments across marketing channels.
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.
This role will be in‑office on a hybrid schedule – Team Members are expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year.
The expected base pay range for this position in the Seattle area is $136,160 - $170,240, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location.