Demonstrated knowledge of credit modeling methodologies, such as structural models (e.g., Merton-type frameworks) and credit rating migration models. Experience with private credit modeling beneficial
Master’s degree with 1-3 years, or Bachelor’s degree with 3-5 years, of practical experience in developing and applying financial econometric models, emphasizing predictive analytics and quantitative risk frameworks
PhD in quantitative fields such as Econometrics, Finance, Mathematics, Statistics, Computer Science, Data Science, or Financial Engineering (in lieu of Master’s with 2+ years also accepted)
CFA or FRM certification preferred
Advanced programming capabilities in Python, R, with practical experience in statistical computing, econometric analysis, and model deployment
Strong conceptual grasp of financial market dynamics and ability to contextualize quantitative insights within real-world macroeconomic and microstructural phenomena
Exceptional verbal and written communication skills in English, with ability to articulate complex quantitative concepts to both technical and non-technical stakeholders
Responsibilities
Design, develop, and enhance BlackRock’s Credit Risk Modeling Suite for private and public credit markets, including models for Probability of Default (PD), Loss Given Default (LGD), and Ratings Migration
Integrate issuer-specific, deal-specific, and market-driven factors into model frameworks for corporate bonds, structured products, and private credit transactions
Stay current with academic research, regulatory developments, and industry best practices in investment-grade, high-yield, and private debt, translating insights into model enhancements
Contribute to the full lifecycle of credit risk models: conceptualization, prototyping, validation, deployment, and performance monitoring
Apply advanced statistical, econometric, and machine learning techniques to challenges like data sparsity, non-linear risk dynamics, and illiquidity premiums in private markets
Conduct model validation, benchmarking, and back-testing using diverse datasets including market data, proprietary deal-level information, and external benchmarks
Implement models in Python within the Aladdin platform for integration with portfolio analytics, risk reporting, and investment decision tools
Enhance modeling infrastructure and workflows using automation, cloud-native technologies, and scalable data pipelines
Drive innovation by incorporating alternative data sources (e.g., ESG metrics, covenant compliance, private financials) and AI/ML techniques to improve predictive accuracy and model coverage
Serve as a thought leader and subject matter expert in internal forums, client engagements, and cross-functional initiatives to promote model adoption and elevate credit analytics