Data Scientist - Algorithms, Mapping

As a Data Scientist on the Mapping team, you will collaborate with our world class team of scientists, engineers, product managers, and designers to grow and improve the quality of recommended routes and accuracy of our travel time estimations. We're looking for a passionate, driven Data Scientist who is excited to dive into our geospatial, behavioural and mobility data, and build a best-in-class mapping product that provides safe, efficient, and seamless navigation for our rideshare drivers.

Data Science is at the heart of Lyft’s products and decision-making. You will leverage data and rigorous, analytical thinking to shape our mapping products and make business decisions that put our customers first. The Mapping team serves models and systems that determine the most efficient routes, fastest travel estimates and process real-time map data signals to detect traffic, closures and slowdowns.

Working with our business and analytics partners, the team owns tools to ensure Lyft offers routes that our users trust. This will involve identifying and scoping opportunities, recommending technical solutions, designing experiments, and measuring the impact of new features. You will help us solve some of the most impactful problems in Mapping, including:

• How do we accurately predict acute and chronic traffic conditions?
• How do we improve the recommendations of our routing algorithms?
• How do we keep our travel estimation promises to our riders and drivers?
• How do we benchmark and measure the success of our services?

Responsibilities:

• Own the complete lifecycle of algorithmic solutions from problem formulation, data exploration, and feature engineering to deployment, monitoring, and iteration
• Prioritize and lead deep dives into our data to uncover new product and business opportunities
• Partner closely with Engineering to build and scale production-grade ML systems, real-time inference services, batch pipelines, and feature stores
• Design, implement, and analyze different types of experiments, and facilitate and foster data-driven and informed decision making and prioritization
• Drive scientific excellence by introducing modern techniques in ML, optimization, reinforcement learning, or graph-based methods to unlock new product capabilities
• Establish metrics that measure the health of our products, as well as rider and driver experience
• Translate complex business challenges into concrete algorithmic solutions in close collaboration with Product, Engineering, Operations, and Science teams

Experience:

• Advanced degree in a quantitative field such as statistics, physics, economics, operations research, neuroscience, or engineering, or relevant work experience
• 3+ years hands-on experience in a data science or machine learning role working with production machine learning models and optimization systems
• Passion for solving unstructured and non-standard mathematical problems
• Experience independently driving multi-project algorithmic scopes and navigating technical ambiguity from ideation to delivery
• Experience with machine learning models in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments
• Strong oral and written communication skills, and ability to collaborate with and influence cross-functional partners
• Working knowledge of modern machine learning frameworks and distributed computing systems, including PyTorch, TensorFlow, Ray, Spark, etc.

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 policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.

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 at least 3 days per week, including 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 Toronto area is CAD $108,000 - CAD $135,000, 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.

Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.