Neo Financial is hiring a Data Science in Calgary!
Neo Financial, a rapidly growing Calgary-based fintech, is building a pipeline of data science talent rather than hiring for one fixed opening. The team sits directly inside live products used by more than a million Canadians, working across credit risk, fraud, marketing and loyalty, executive decision-making, and the ML platform that keeps models running in production. This is not a reporting function or a side lab: data scientists, analytics leaders, and ML platform engineers operate as one embedded group, and the expectation is that work moves from an ambiguous business question all the way to a monitored, trustworthy service. The posting invites senior individual contributors, analytics leaders, and platform builders to apply so the company can reach out when a current or emerging need matches their background. It is explicitly ongoing and does not guarantee an interview or commit a candidate to a specific role. The culture is described as fast-paced, high-standard, and low-ego, with heavy cross-functional partnership and in-person collaboration across the Calgary, Winnipeg, and Toronto offices.
Key responsibilities
- Own a substantial surface area such as risk, fraud, growth, analytics strategy, or the ML platform that productionizes models
- Carry the full lifecycle: framing the problem, choosing the method, delivering the work, and validating and monitoring it after launch
- Establish durable standards around reproducibility, metric consistency, model governance, and documentation instead of relying on one-off efforts
- Raise the technical bar through code review, sound experimental design, and mentoring colleagues
- Translate between Product, Engineering, Risk, Marketing, Finance, and operations so models actually ship and insights change decisions
- For analytics leaders: set strategy, build the team, and shift the company from reactive reporting to decision-shaping insight
- For platform builders: own pipelines, observability, drift detection, model registries, and infrastructure-as-code from experiment to production
Key requirements
- Strong hands-on Python and SQL, plus enough cloud and data-platform fluency to build and ship rather than just present
- Experience in fintech, lending, payments, or another regulated setting where incorrect numbers carry real consequences
- A record of moving work from functional to reliable to scaled, navigating ambiguity rather than sidestepping it
- For senior data scientists: end-to-end ownership of model design, training, validation, and production, with the ability to defend a validation strategy and explain results to executives
- For analytics leaders: demonstrated ability to set direction, grow a team, and hold firm on metric definitions and experimentation quality under pressure
- For ML platform engineers: production ML on cloud infrastructure, treating reliability as part of the model
- Low ego paired with high standards and willingness to do unglamorous work
- Security screening including criminal records and credit checks
- Seniority: Senior / Staff / Principal (ongoing talent pipeline)
- Work mode: In-person collaboration across Calgary, Winnipeg, and Toronto offices
- Key skills: Python, SQL, Cloud infrastructure, Production ML, Model validation, Model monitoring and drift detection, ML platform and pipelines, Infrastructure-as-code, Experimental design, Model governance, Fintech or regulated-industry experience, Cross-functional communication, Mentorship
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