Data Science Intern, Algorithms (Summer 2027 - Toronto)

<p>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.</p> <p><span style="font-weight: 400;">Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for <span style="text-decoration: underline;">Masters or PhD students</span> who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment.</span></p> <p><span style="font-weight: 400;">We are hiring for a variety of Data Science interns, focusing on the following specialties:&nbsp;&nbsp;</span></p> <ul> <li><strong>Optimization: </strong><span style="font-weight: 400;">Construct and fit statistical or optimization models that facilitate automated decision making in the app.</span></li> <li><strong>Machine Learning:&nbsp;</strong>Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment.</li> <li><strong>Inference:&nbsp;</strong>Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems.</li> </ul> <p><span style="font-weight: 400;">You will report into a Science Manager.</span></p> <h2><strong>Responsibilities:</strong></h2> <ul> <li style="font-weight: 400;"><span style="font-weight: 400;">Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Perform exploratory data analysis to gain a deeper understanding of the problem</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Write production modeling code; collaborate with software engineers to implement algorithms in production</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Design and run both simulated and live traffic experiments</span></li> <li style="font-weight: 400;"><span style="font-weight: 400;">Analyze experimental and observational data; communicate findings inclu