Senior Mortgage Credit Modeler

RiskSpan • WV

109d

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

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.

What This Job Offers

• Number of Employees: 101-250 employees