Data Scientist, RTSM Algorithms

At Lyft, our mission is to enhance people's lives by providing the best transportation in the world. To achieve this, we begin within our own organization, creating an open, inclusive, and diverse environment. The Marketplace team at Lyft is tasked with propelling the company's growth and achieving key company business and financial metrics, and marketplace performance. The team provides analysis and use cases to assist Product teams in the development of automated tools for managing growth investments. We are seeking a Data Scientist with expertise in Economics, Statistics, Machine Learning, or other quantitative fields to join the Pricing division of the Marketplace team, which oversees ride-sharing pricing. Your analytical, modeling, and experimentation skills will help shape current and future roadmaps as you develop domain expertise in cutting-edge pricing theory. We are in pursuit of a data and process-focused individual who pays meticulous attention to detail and is known for their analytic problem-solving abilities. Responsibilities: - Collaborate with other scientists and colleagues in the pricing team, and frame problems mathematically and in a business context with external teams - Perform exploratory data analysis to understand problems more deeply - Develop and adjust statistical, machine learning, or optimization models - Write production model code; collaborate with Software Engineers for the implementation of algorithms in production - Design and execute simulated and live experiments - Analyze experimental and observational data; report findings; facilitate launch decisions Experience: - A Master's or Ph.D. in Economics, Statistics, Operations Research, Mathematics, Computer Science, or another quantitative field - At least 3 years of professional experience for Ph.D. holders, or 3-5 years for Master’s degree holders in a data scientist role - Passion for solving non-standard, unstructured mathematical problems - Comprehensive experience with data, including querying, aggregation, analysis, and visualization - Proficiency in Python, or another interpreted programming language such as R or Matlab - Proficiency in SQL - ability to write structured and efficient queries on large data sets - Ability to cooperate and communicate with others to solve issues - Strong oral and written communication skills, and the ability to collaborate with cross-functional members Benefits: - Excellent medical, dental, and vision insurance options - Mental health benefits - Family building benefits - In addition to 12 observed holidays, salaried employees are entitled to unlimited paid time off, while hourly workers receive 15 days paid time off - 401(k) plan to help future savings - 18 weeks of paid parental leave, applicable for biological, adoptive, and foster parents - 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 potential employees will be taken into consideration without regard for race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other prohibited basis. We also consider applicants with criminal histories consistent with federal, state, and local law. This role will function in a hybrid style, with at least 3 days of in-office work expected on Mondays, Thursdays, and a team-specific third day. Additionally, hybrid roles offer the flexibility to work remotely for up to 4 weeks annually. The expected salary range for this position in the San Francisco area lies between $162,000 and $180,000. Salaries are dependent on various factors such as qualifications, experience, and geographical location. This range does not include potential equity offerings, bonuses, and benefits. Your recruiter can provide more information about the salary range corresponding to your working location and other factors during the hiring process.