Data Scientist, Algorithms- Experimentation

Full Time
San Francisco, CA, USA
9 months ago

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/tools that power our internal and external products. 

We are looking for a Data Scientist to join the Experimentation team. Experimentation is the foundation for decision making at Lyft. The team builds state-of-the-art experimentation methodologies and runs a powerful platform that measures the impact of every product change at Lyft. As an experienced data scientist, you will use your experimental design and causal inference skills to improve how Lyft measures the short and long-term impact of a change in a dynamic marketplace environment. You’ll partner closely with product, engineering, and business leaders to build and scale our products and systems, shape the long-term strategy and deliver business goals.

To learn more about the Experimentation team’s work, see Parameter Exploration at Lyft and Challenges in Experimentation.

Responsibilities 
  • Collaborate with stakeholders from a diverse set of business lines to design new experimentation frameworks to improve Lyft’s decision making efficiency and quality. Advise teams on best experimentation practices. Be a thought leader and go-to expert for experiment users
  • Create success criteria for Lyft’s core products: build a framework for trading off various business metrics and unify the shipping criteria across various products
  • Build statistical models, pipelines and systems to better measure the true short and long term effects on the marketplace
  • Provide technical guidance for the team
Experience 
  • Advanced degree in a quantitative field like statistics, economics, computer science, operations research, or engineering; or relevant work experience
  • 4+ years of hands-on industry experience in causal inference or data science
  • Track record of using statistics and guiding teams through unstructured technical problems to deliver business impact
Benefits
  • Great medical, dental, and vision insurance options
  • Mental health 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 base pay range for this position in the San Francisco area is $139,000 - $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.