The Marketplace team at Lyft is responsible for accelerating the growth of the business while simultaneously delivering on key business and financial metrics, and Dynamic Pricing & Offer Selection is an integral part of that. Our team is the dynamic pricing team responsible for leveraging both real-time and historical data to set prices for every single ride at Lyft while balancing prices and ETAs to better serve our riders' needs.
We are looking for fast learning, data-driven individuals who are passionate about tackling these open-ended problems in pricing theory and making a large, positive impact in the business. In this role, you will work closely with Product and Engineering on a cross-functional team to prototype and build end-to-end dynamic pricing models that can operate at scale. In addition, you will be expected to leverage your domain knowledge and influence existing and future roadmaps to help Lyft continue to deliver a better experience for riders and drivers.
Responsibilities:- Partner with other scientists, colleagues in the pricing team, and with external teams to formalize problems mathematically and within the business context
- Perform complex data analysis to gain a deeper understanding of problems by identifying their root causes
- Develop and fit statistical, machine learning, or optimization models
- Write production code; collaborate with Software Engineers to ship models to production
- Design and implement both simulated and live experiments
- Analyze experimental and observational data; facilitate launch decisions
- Communicate findings to a broad audience consisting of Product, Engineering, and executive leadership
Experience:- Data Scientist with experience in Machine Learning, Statistics, Economics, or other quantitative fields
- M.S. or Ph.D. in Computer Science, Economics, Statistics, Mathematics, or other quantitative fields
- 1-4+ years of professional experience for PhDs or 3-5+ years for Master's in a Data Science role
- Passion for solving unstructured and non-standard mathematical problems
- End-to-end experience with data, including querying, aggregation, analysis, and visualization
- Proficiency with Python, or another interpreted programming language like R or Matlab
- Proficiency in SQL - able to write structured and efficient queries involving multiple large data sets
- Ability to collaborate and communicate with others to solve a problem
- Strong oral and written communication skills, and ability to collaborate with cross-functional partners
- Experience in causal inference, LTV modeling, econometrics, or user choice modeling preferred
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 $128,000 - $160,000, 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.
