Data Science

Join us to build a more rewarding financial future for all Canadians

At Neo, we’re on a mission to build a more rewarding financial future for all Canadians. Life at a rapidly scaling tech startup isn’t for everyone. It’s complex, fast-paced, high-pressure, but also incredibly fulfilling. Since its founding in 2019, Neo has gained incredible traction and is one of the fastest-growing fintech companies in Canada.

• #1 on Deloitte’s Technology Fast 50 for 2023, 2024, and 2025 — the first company ever to achieve a three-peat at the top!

• #1 Fastest Growing Company in Canada for 2024 by Globe & Mail

• Top-ranked mobile apps and credit cards

• Team of 500+ people

• 1M+ customers

• 10K+ retail partners

This posting is intentionally ongoing. It is not a single open role with a start date attached. As Neo's data science organization grows, we keep a pipeline of people we'd want on the team and reach out when a current or emerging opportunity fits.

The Role

Neo's data science org sits inside products millions of Canadians already use. Data scientists, analytics leaders, and ML platform engineers work as one embedded function: production models, insight that shapes decisions, and the path from prototype to a monitored service. Not a reporting desk. Not a lab on the side.

There's a version of this work that stops at a notebook, a dashboard, or a handoff. Future roles here don't. They run from an ambiguous business question to something that actually runs — through the data, the constraints, and the commercial trade-offs.

At this level you frame the problem, ship the work, and stand behind the result. The people in this pipeline have taken models from working to reliable to material. They know a clever fit from a system the business can trust.

The work

Neo is rebuilding financial services from scratch. We've shipped a full-stack banking platform (credit cards, savings, mortgages, personal loans, cash back) at a pace most financial institutions can't match in a decade. Data science is inside that loop, not adjacent to it.

The problems span credit risk, fraud, marketing and loyalty, executive decisioning, and the ML platform that keeps models alive in production. They run against real financial outcomes for millions of Canadians. Correctness isn't optional. Neither is commercial impact. The work doesn't get easier as we scale.