VP, Credit Risk Modeling

The Opportunity

Global Atlantic, a KKR company, is one of the largest insurance and reinsurance platforms in Bermuda, managing over $110 billion across multiple entities. As the portfolio grows in scale and complexity — spanning structured credit, mortgage loans, corporate bonds, and alternative assets — we are investing in a dedicated credit modeling capability to help the firm understand and quantify tail credit risk across the full investment book. This VP role will lead the development of models that measure portfolio-level default and downgrade exposure, inform capital allocation, and strengthen our risk framework.

Responsibilities:

• Build and own portfolio credit risk models that quantify tail losses from default and rating migration across asset classes
• Develop a credit risk framework: calibrate transition matrices, model correlated credit migration, and produce full loss distributions to measure tail risk at the portfolio level
• Calibrate asset-class-specific inputs — transition probabilities, loss given default, recovery rates, and credit spreads
• Translate model outputs into actionable capital metrics: compute expected loss, cost of downgrade, and tail risk measures by rating and tenor to support portfolio construction, and limit-setting decisions
• Build production-quality Python pipelines for model execution, data processing, and automated reporting; deliver clear visualizations and summaries for senior leadership and the Board
• Partner with investment teams, and finance to embed credit risk analytics into portfolio monitoring, stress testing, and strategic asset allocation

Qualifications Required:

• 8–12 years in credit risk modeling, quantitative finance, or insurance capital modeling.
• Deep expertise in portfolio credit risk frameworks — transition matrices, Monte Carlo simulation, correlated default modeling, and tail risk measurement.
• Production-quality Python skills.
• Experience calibrating and validating credit models.
• Strong written communication for technical and executive audiences.
• Comprehensive user of AI tools.

Preferred:

• Insurance regulatory capital experience (Bermuda, Solvency II, or NAIC RBC).
• Structured credit modeling (CLO engines, CMBS/RMBS loss models).