Data Scientist, Algorithms, Optimization - Fulfillment
Lyft · New York City, NY · Fulfillment
About this role
Lyft is hiring a mid-level Data Scientist based in New York City, NY. The posting calls out experience with Python, Data Structures, Machine Learning, Data Analytics and roughly 2+ years of relevant work. Listed education preference: a master's degree or equivalent. Compensation is listed at $128,000–$160,000 per year.
- Role
- Data Scientist
- Function
- data engineering
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- New York City, NY
- Experience
- 2+ years
- Education
- Master's degree
- Department
- Fulfillment
More roles at Lyft
Job description
from Lyft careersAt 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 central to Lyft's products and decision-making. As a Data Scientist on the cross-functional team, you will work in a dynamic environment, tackling a variety of problems from shaping critical business decisions to building algorithms that power our products. We seek passionate, driven Data Scientists to address some of the most interesting and impactful problems in ridesharing.
As a Data Scientist specializing in Algorithms, you will develop mathematical models for the platform's core services, addressing diverse problems in optimization, prediction, machine learning, and inference. On the Fulfillment team, you will collaborate with cross-functional teammates and stakeholders to enhance algorithms for matching rideshare supply and demand in real time and develop product offerings to improve the experiences of Lyft Riders and Drivers.
Responsibilities:
- Leverage data and analytic frameworks to direct creations and improvements of algorithms and models underpinning the team’s systems and products
- Partner with Engineers, Product Managers, and Business Partners to frame problems, both mathematically and within the business context.
- Perform exploratory data analysis to gain a deeper understanding of the problem