AI Quantitative Risk Modeler

Quelion Intelligence

Remote · $200k+ / year · Full-time · Posted 4 months ago

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About the role

The 2008 financial crisis was partly a failure of risk models that assumed normal distributions and ignored tail correlations. Our mission is to build the next generation of quantitative risk infrastructure — models that measure what they claim to measure, quantify uncertainty honestly, and remain valid under stress scenarios that have never occurred before. We serve 12 tier-1 investment banks and 40 asset managers. Our models estimate credit risk, market risk, and liquidity risk at portfolio scale, producing outputs that feed directly into capital allocation decisions and regulatory reporting. When our models are wrong, clients lose money or misreport to regulators. The bar is extremely high. You'll lead model development for our credit risk product line, working with a team of two quants and one validation engineer. You'll design novel approaches to default prediction under regime changes, build stress testing frameworks that go beyond historical scenarios, and defend methodology choices to sophisticated clients who know what good looks like.

Responsibilities

Lead credit risk model development for tier-1 bank and asset manager clients
Design default prediction models that perform under economic regime changes
Build stress testing frameworks extending beyond historical scenario sets
Implement model validation frameworks meeting regulatory standards
Present methodology to client risk committees and internal model risk teams
Research and evaluate emerging methods from academic literature

Requirements

7+ years in quantitative finance or ML applied to financial risk
Deep expertise in credit risk modeling, survival analysis, or default prediction
Strong Python and statistical computing skills
Understanding of regulatory frameworks: Basel III, IFRS 9, CECL
Track record presenting model methodology to sophisticated technical audiences