Data Scientist

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.

Data Science is at the heart of Lyft’s products and decision-making. Data Scientists at Lyft operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges - from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Driver Incentives Science owns the algorithms and systems behind incentive design, influencing driver engagement and marketplace efficiency — from real-time supply positioning to longer-horizon earnings and engagement programs. The team is responsible for designing pay and incentive mechanisms that are efficient and good for driver experience over the long run.

As a Data Scientist specializing in Algorithms, you'll partner closely with product, engineering, and operations leaders to build and scale incentive systems, shape long-term mechanism design strategy, and deliver on critical business goals tied to marketplace efficiency and driver earnings. Candidates with strong optimization backgrounds — think mathematical programming, control theory, or operations research — are a great fit, though we welcome strong candidates from machine learning or causal inference as well. The ideal candidate thrives in a fast-paced environment and brings a hands-on, entrepreneurial mindset to drive results.

Responsibilities:

• Collaborate with engineering and product teams to design, implement, and iterate on new features and algorithmic improvements for driver incentives and pay mechanisms.
• Design, develop, and deploy optimization models, algorithms, and systems for problems such as budget allocation, multidimensional cost-curve development, and incentive targeting.
• Write production model code; collaborate with Software Engineers to implement algorithms in production.
• Perform exploratory data analysis to gain a deeper understanding of the marketplace and its users.
• Communicate findings and facilitate launch decisions with technical and non-technical stakeholders.
• Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies.

Experience:

• Advanced degree (MS or PhD, PhD preferred) in a quantitative field like Operations Research, Applied Math, Computer Science, Statistics, Engineering, or a related area; or equivalent work experience.
• Passion for solving unstructured and non-standard mathematical problems, with 2+ years of hands-on experience in optimization (preferred), causal inference, or machine learning.