Data Scientist - Algorithms, Mapping

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.

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,