As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions. You will leverage your skills to build ML and optimization models and productionalize pipelines that can scale to millions of calls per day while solving critical business problems that have a big impact on the marketplace and rider experience. You will get exposure to a diverse set of real-world problems across optimization, prediction, machine learning, and inference and collaborate closely with teammates and stakeholders across Pricing, from Product Managers to Engineers and Analysts.

We are looking for someone who is excited about working in a fast-paced, innovative, and impactful environment, and is adept at balancing complexity and efficiency to translate real world business problems into reliable solutions, systems and decision frameworks.
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. Able to build 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 the robustness of the systems
• 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

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 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 San Francisco area is $193,600 - $242,000, 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.