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Lyft

Senior Data Scientist, Causal Inference

Posted 12 Days Ago
Be an Early Applicant
In-Office
New York, NY, USA
148K-185K Annually
Senior level
In-Office
New York, NY, USA
148K-185K Annually
Senior level
Lead causal inference and marketing mix modeling to measure and optimize marketing investments. Define problems with stakeholders, build statistical pipelines and production models, design and analyze experiments, deploy solutions, and support automated campaigns via science on-call rotation to drive Growth outcomes.
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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.

Responsibilities:
  • Deliver results across the entire lifecycle of data science solutions for Growth: from defining the problem with cross-functional stakeholders to deploying production models that address key business problems.
  • Own complex domains and develop long-term roadmaps to maximize business impact.
  • Build statistical pipelines, write production code, and design/analyze experiments.
  • Participate in the science on-call rotation to ensure automated campaigns operate successfully.
Experience:
  • Advanced degree in statistics, economics, mathematics, or equivalent industry experience.
  • 4+ years of industry experience in causal inference or data science.
  • Proven ability to apply statistics to unstructured problems and deliver measurable results.
  • Deep technical expertise in causal inference and tackling challenging measurement problems.
  • Expertise in marketing mix modeling is highly preferred.
  • Expertise in SQL and experience with large-scale data platforms.
  • Proficiency in Python and working within production coding environments.
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 with company match 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
  • Monthly Lyft credits and complimentary Lyft Pink membership

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 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

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