VP, Credit Risk Modeling

KKR

$160K — $175K *
Finance & Insurance
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • 8-12 years in credit risk modeling or quantitative finance
  • Expertise in portfolio credit risk frameworks including transition matrices
  • Strong proficiency in production-quality Python programming
  • Experience with calibrating and validating credit models
  • Excellent written communication skills for diverse audiences
  • Familiarity with AI tools for enhanced analytics

Responsibilities

  • Develop and maintain portfolio credit risk models for tail loss quantification
  • Create a comprehensive credit risk framework including transition matrices and loss distributions
  • Calibrate critical asset-class inputs like recovery rates and loss given default
  • Translate model outcomes into actionable metrics for capital allocation and limit settings
  • Implement Python pipelines for model execution and reporting
  • Collaborate with investment and finance teams to integrate credit analytics into portfolio strategies

Benefits

  • Comprehensive health insurance coverage
  • 401(k) retirement plan options
  • Generous paid time off policy
  • Professional development opportunities
  • Access to wellness programs
  • Discretionary bonuses based on performance
Full Job Description
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).


This is the expected annual base salary range for this New York-based position. Actual salaries may vary based on factors, such as skill, experience, and qualification for the role. Employees may be eligible for a discretionary bonus, based on factors such as individual and team performance. Base Salary Range - $160,000 to $175,000

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