An interview with a quantitative risk analyst
March 2026
Describe your typical day as a quantitative risk analyst in Montreal.
My mornings start with checking overnight market movements and running updated volatility models. But it’s never just about the output. I spend a lot of time preparing to explain our results to the team—what changed, why, and where the uncertainties are. Building trust means being transparent about every step, including what we don’t know yet. The field isn’t kind to guesswork.
What are the most rewarding and challenging aspects of your current role?
The best moments are when my technical work leads to a real discussion within the team—when someone asks why a model shifted, and we dig in together. The hardest part is admitting when our models fall short. In volatility forecasting, overconfidence can be costly. Transparency—about process, assumptions, and limitations—earns more respect than pretending to have all the answers.
How does machine learning impact your workflow compared to more traditional models?
Machine learning is a double-edged sword. It finds patterns that traditional methods might miss, but it’s easy to lose sight of how those patterns were derived. I keep a written log of model changes and rationale, so I can justify every decision. Our team culture expects that—documentation is as important as the code itself.
What advice would you give to someone entering this field?
My advice to newcomers: never sacrifice clarity for complexity. If you can’t explain your work simply, you probably don’t understand it yet. Don’t be afraid to document failed attempts—they teach you more than the successes.
How has your approach to volatility analytics changed over time?
When I started, I thought technical skill alone was enough. I’ve learned that building credibility is about showing your process, not just your results. The field moves quickly, but trust takes time to earn. That’s what lasts.