Data Science Manager- Pricing

Vollzeit
San Francisco, CA, USA
vor 2 Monate

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. Data Scientists at Lyft work in dynamic environments, where we embrace moving quickly to build the world’s best transportation. We take on a variety of problems ranging from shaping long-term business strategy with data, making short-term critical decisions, and building algorithms/models that power our internal and external products. 

We are looking for a Data Science Manager in the Pricing Team. As the data science manager, you’ll help develop the vision, set roadmaps, and lead execution for projects. You’ll partner closely with product, engineering, analytics, and business leaders to build and scale our products and systems, shape the long-term strategy and deliver business goals. The ideal candidate should have strong modeling experience in Machine Learning, Optimization and Causal Inference, embrace moving fast with strong business acumen and a deep understanding of our business and our riders.  

Responsibilities: 
  • Lead and grow a high-performing team of data scientists with various backgrounds including analytics, experimentation, machine learning and causal inference.
  • Be a thought leader and go-to expert for goals, strategy, and long-term vision
  • Provide coaching and technical guidance for the team
  • Prioritize and lead deep dives into our data to uncover new product and business opportunities
  • Facilitate and foster data-driven and informed decision making and prioritization
Experience: 
  • Advanced degree in a quantitative field like computer science, statistics, economics, operations research, or engineering;  or relevant work experience
  • 5+ years of hands-on technical experience in machine learning, causal inference, or data science
  • 2+ years of management experience building and leading data science teams
  • Strong track record of using machine learning / causal inference and data science to improve business outcomes
  • Track record of guiding teams through unstructured technical problems to deliver business impact
  • Hands-on experience with large-scale machine learning engineering & systems is a huge plus
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 $172,000 - $215,000. 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.