Intern, Data Scientist - Fall 2026

Company: Capital One

Location: Toronto, ON

Type: Full-time

Posted: 2026-04-27

About this role

161 Bay Street (93021), Canada, Toronto,Toronto, Ontario,Intern, Data Scientist - Fall 2026

### About Capital One Canada.

For 30 years, we’ve been on a mission to change banking for good and build relationships by making credit accessible, simple, intuitive and rewarding. We want to help Canadians succeed with credit, because we believe in people — in our customers, in our associates, and in talent like you!

### About the Team

Capital One is more than you think. We're a data-driven tech company that just happens to do credit cards. We focus on customer-first solutions, and we're united with this common goal. We have all the advantages of a startup, with the resources of a big company. Working here means you're curious, innovative spirit will be encouraged as you push boundaries and try new things, while your career is nurtured and supported. Our environment is creative and filled with teams that are passionate about the dynamic work they're doing. We're ahead of other banks in tech and we want to keep it that way.

### About the Role

As an Intern, Data Scientist, you will work with cross-functional teams to leverage model solutions to solve complex business problems. As a data savvy individual, you will use your skills to identify the best algorithm or model to solve the problem at hand. You will have autonomy to take ownership and lead an end to end project, from problem identification to solution deployment.

Types of projects the Data Science team is involved in:

  • Using Python and PySpark to analyze millions of transactions to understand and predict fraud patterns.
  • Exploring new proprietary data sources and creating predictive models or customer behavior.
  • Applying machine learning techniques to build financial risk models.
  • Create and test algorithms to optimize search engine marketing.
  • Design and develop data pipelines and visualizations to monitor model performance and data quality.

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