Data Scientist, Algorithms, ML - Fulfillment

At 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:

• Drive the Science and Machine Learning roadmap of the team’s problem area, 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
• Construct and fit statistical, machine learning, or optimization models
• Write production modeling code; collaborate with Software Engineers to implement algorithms in production
• Design and implement both simulated and live traffic experiments
• Analyze experimental and observational data; communicate findings; facilitate launch decisions
• Develop measurement methodologies to monitor the health of our products, as well as the impacts on user outcomes and marketplace outcomes
• Drive collaboration and coordination with cross-functional teams
• Advise teams on best practices. Be a thought leader and go-to expert for stakeholders and dependency teams

Experience:

• M.S. or Ph.D. in Machine Learning, Statistics, Computer Science, Mathematics, or other quantitative fields
• 2+ years professional experience in a technology company setting
• Proven experience with building and evaluating machine learning models
• Proficiency with Python and working in a production coding environment
• Past experience working as a Machine Learning Engineer is a preferred plus
• Passion for solving unstructured and non-standard mathematical problems
• End-to-end experience with data, including querying, aggregation, analysis, and visualization
• Strong oral and written communication skills, and ability to collaborate and communicate with others to solve a problem

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Child care and pet benefits
• Access to a Lyft funded Health Care Savings Account
• RRSP plan with company match to help save for your future
• In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service 
• Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
• Subsidized commuter benefits and Lyft ride credits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company