RiskSpan
Overview
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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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.
#J-18808-Ljbffr