Data Science

Data gives us the essential feedback to drive Canva’s exponential growth, from analytics and experiments to search and recommendations.

How our data team works

Data is playing an increasing role across all areas of Canva. Whether you’re involved in machine learning, product analytics or data infrastructure, you’ll work on a small and empowered team and have a huge impact on our growing products.

It’s all about prioritising and thinking creatively about what projects we should be working on next. At Canva we always try to make sure there’s a strong match between your projects and your interests.

We’ve started a lot of exciting projects, but there’s just so much scope in terms of future development.

Robbie Hazelwood
Data scientist

Why Canva is a good place to be a data scientist

When we perform experiments, we can expect improvements in an order of magnitude that’s not possible at a larger company. Anyone coming into a data scientist or machine learning role at Canva can have a massive impact. It’s an exciting product, and from an engineering perspective we have a lot of cool toys to play with!

Robbie Hazelwood
Data scientist
  • Machine learning

    Our Machine Learning Engineers are applying modern data engineering principles to power the Canva experience. The team are building advanced search and recommendations technology to return intuitive and personalised results.

  • Product analytics

    Analytics inform almost every function at Canva. Our product analysts comb our data for the largest growth opportunities, conduct experiments and push the needle on our key metrics.

  • Powerful data infrastructure

    Our data infrastructure centralises information from across the company, creating the engine room to power every function at Canva.

Some advice to people applying as a data scientist at Canva

We’re looking for people who are interested in the problems we’re solving, are optimistic about the impact they can have on the product and the impact that Canva can have on the world. In terms of the take-home challenge, just be really good at the fundamentals and make sure you can do engineering around that. We’re not going to be mean to you!

Robbie Hazelwood
Data scientist