Scovai
Quantitative Risk Director — Financial Services
Skills
2 months ago
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Important information
Contract type:
Permanent
Location:
Chicago, IL, USA
Work mode:
Onsite
Published on:
30 September 2026
What they need
We are conducting a confidential search for a Quantitative Risk Director to lead model risk and quantitative risk management for a financial services organization based in Chicago, working hybrid. This is a director-level opportunity for a quant risk leader who can own model validation, capital adequacy analytics, and regulatory stress testing.
In this role, you will lead the model validation function across credit, market, and counterparty risk models, own the methodology for Value-at-Risk (VaR) and expected shortfall calculations, and direct the bank's participation in CCAR/DFAST and internal stress-testing cycles. You will oversee model risk governance under SR 11-7/Basel frameworks, manage relationships with regulators and internal audit on model risk matters, and ensure model inventories, documentation, and back-testing meet regulatory expectations.
You will build and lead a team of quantitative analysts and model validators, partner with front-office quants and risk technology teams on model implementation, and present risk methodology and results to senior management and board risk committees. You will also drive the roadmap for risk analytics infrastructure and machine-learning-based risk models where applicable.
Nice to Have:
- Experience with CCAR/DFAST submission cycles at a bank or systemically important institution
- Advanced degree in a quantitative field (Financial Engineering, Statistics, Mathematics, Physics)
- FRM or PRM certification
- Experience integrating machine learning into traditional risk models
- Direct experience presenting to board-level risk committees
Nice to have
- Experience with CCAR/DFAST submission cycles at a bank or systemically important institution
- Advanced degree in a quantitative field (Financial Engineering, Statistics, Mathematics, Physics)
- FRM or PRM certification
- Experience integrating machine learning into traditional risk models
- Direct experience presenting to board-level risk committees
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