Applied Scientist, Machine Learning and Operations Research, Pricing

About the Role

As an Applied Scientist specializing in Machine Learning and Operations Research on the Pricing team, you will develop mathematical models and launch algorithms that power key pricing and ETA decisions for Lyft's rideshare products. You will build ML and optimization models and productionalize pipelines that can scale to millions of calls per day, solving critical business problems with a significant impact on the marketplace and rider experience. This role offers exposure to a diverse set of real-world problems across optimization, prediction, machine learning, and inference, and requires close collaboration with Product Managers, Engineers, and Analysts. We are seeking someone who thrives in a fast-paced, innovative, and impactful environment, adept at balancing complexity and efficiency to translate business problems into reliable solutions.

Responsibilities

• Partner with Data Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context.
• Write production quality code. Design, build and deploy production-grade ML and Optimization models, including custom methods and tooling beyond off-the-shelf libraries.
• Perform data analysis and build proof-of-concepts to explore and propose ML and Optimization solutions to both new and existing problems.
• Evaluate machine learning systems against business goals. Collaborate with Engineers to implement algorithms in live systems and ensure their robustness.
• Establish metrics and development measurement methodologies to monitor the health of our products, as well as the impacts on user and marketplace outcomes.
• Drive collaboration and coordination with cross-functional teams.

Experience

• M.S. or Ph.D. in Machine Learning, Operations Research, Statistics, Computer Science or other quantitative fields.
• 2+ years of algorithms experience in a technology company setting.
• Proficiency with Python and working in a production coding environment.
• Passion for solving unstructured and non-standard mathematical problems and building impactful machine learning models leveraging expertise in one or multiple fields.
• Strong understanding of machine learning methodologies, with proven experience with building and evaluating optimization or machine learning models.
• Strong verbal and written communication skills with a good track record of collaborating with others to solve a problem.