- Senior
- Bureau à Arlington
Lead Mortgage Credit Modeler
Role Summary
We are seeking a quantitative modeler with deep expertise in mortgage credit risk to design and implement advanced statistical and econometric models. This role will focus on loan-level performance modeling (delinquency, prepayment, default, loss given default) and structured mortgage asset valuation. The ideal candidate will combine rigorous quantitative training with hands-on experience in coding, model development, and empirical research.
Core Responsibilities
- Develop and enhance loan-level mortgage credit risk models (transition matrices, hazard models, competing risks, survival analysis).
- Implement econometric and machine learning approaches for prepayment, default, and severity modeling.
- Conduct back-testing, out-of-sample validation, and sensitivity analysis to assess model robustness.
- Analyze large-scale loan-level datasets (e.g., GSE loan-level, CoreLogic, Intex, private-label RMBS).
- Build and document models in Python/R/C++, ensuring reproducibility and version control.
- Partner with structured finance and risk teams to integrate models into pricing, stress testing, and risk management frameworks.
- Research macroeconomic drivers of mortgage performance and their incorporation into stochastic scenario design.
- Author technical model documentation and research notes for internal stakeholders, model risk management, and regulators.
Technical Qualifications
Required:
- Master’s or Ph.D. in Quantitative Finance, Statistics, Econometrics, Applied Mathematics, or related quantitative discipline.
- 7+ years of direct experience in mortgage credit risk modeling or structured finance analytics.
- Advanced skills in statistical modeling: survival analysis, proportional hazard models, logistic regression, generalized linear models, panel data econometrics.
- Strong programming expertise in Python (pandas, NumPy, scikit-learn, statsmodels) or R.
- Proficiency in handling big data (SQL, Spark, Snowflake and cloud-based data environments).
- Deep knowledge of mortgage credit risk dynamics, housing market fundamentals, and securitization structures.
Preferred:
- Experience with Hierarchical models, and Monte Carlo simulation.
- Knowledge of machine learning algorithms (e.g., gradient boosting, random forests, neural nets) applied to credit modeling.
- Familiarity with stress testing frameworks and regulatory model governance needs.
- Background in RMBS cash flow modeling and structured product analytics.
This role is highly technical and research-driven. Candidates should be comfortable working with complex datasets, formulating empirical hypotheses, and coding full modeling pipelines from data ingestion through validation and deployment.
About RiskSpan
RiskSpan is a leading source of analytics, modeling, data, and risk management solutions for the Consumer and Institutional Finance industries. We help financial institutions and regulators solve complex problems involving market, credit, and operational risk. Our clients include top banks, asset managers, servicers, and government-sponsored enterprises.
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