Sr. Quantitative Analyst – Model Risk Management
Posted Sep 10, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
How do you write a validation report so a model owner can act on it rather than argue with it?
What they are checking: Whether they write validation reports that model owners can act on, because findings only reduce risk when they actually lead to fixes.
What would you do
You write a finding and the model owner disagrees. Walk me through what happens next.
What they are checking: What happens when the finding is unwelcome, in a second line role
About the work
This role independently validates statistical, econometric, machine learning and expert judgment models. Walk me through a validation you ran end to end.
What they are checking: Whether they have run full model validations independently, rather than supporting others, since this senior role expects them to own the work.
What does conceptual soundness mean when you're validating a model, and how do you test it?
What they are checking: Whether they truly understand conceptual soundness and know how to test it, since the posting names it as a core validation dimension.
How do you build a benchmark model, and what does it tell you that testing the original doesn't?
What they are checking: Whether they know how to build and use a benchmark model, since comparing against an alternative reveals weaknesses that testing alone can miss.
How does your validation approach change with the model's risk tier?
What they are checking: Whether they scale validation depth to a model's risk tier, because spending equal effort on every model wastes time and misses where risk concentrates.
What's different about validating a machine learning or artificial intelligence model compared with a regression?
What they are checking: Whether they understand what makes machine learning models harder to validate than regressions, an area the posting flags as a preferred skill.
The posting names supervisory guidance on model risk management. What does that guidance actually require of a bank?
What they are checking: Whether they understand what supervisory model risk guidance actually requires, since the whole validation process exists to meet those regulatory expectations.
You'd recommend ways to automate the end-to-end validation process. What would you automate first, and what should stay manual?
What they are checking: Whether they can judge which validation steps benefit from automation and which need human judgment, since the posting asks them to recommend improvements.
The posting asks for a doctorate or master's in mathematics, statistics, economics or another quantitative field, one to three years in model development or validation, and one to two years with Python, based in Rosemont. Where do you sit?
What they are checking: Whether their degree, validation experience and Python skills meet the stated qualifications, so both sides can judge the fit honestly from the start.
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Practice this out loudThe full job description
As published by Wintrust.