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.
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.
We don’t have a handoff culture. You partner with Product, Engineering, Risk, Marketing, Finance, and operations. Models ship. Insights change decisions. You own the path from prototype to something the company can run on.