SMBC Group
Quantitative Credit Risk Modeler Vice President
SMBC Group, Jersey City, New Jersey, United States, 07390
Overview
Quantitative Credit Risk Modeler Vice President at SMBC Group in New York City. The role focuses on quantitative model development, validation, remediation, and maintenance of advanced credit risk models for wholesale and commercial portfolios, ensuring alignment with regulatory requirements (CCAR/DFAST, CECL, Basel III/IV) and business objectives. The ideal candidate will have strong technical modeling expertise, a deep understanding of regulatory frameworks, and the ability to collaborate across risk, finance, and technology teams.
The anticipated salary range for this role is between $155,000 and $195,000. The specific salary offered to an applicant will be based on their qualifications, experiences, and an analysis of the current compensation paid in their geography and the market for similar roles at the time of hire. The role may also be eligible for an annual discretionary incentive award. In addition to cash compensation, SMBC offers a competitive portfolio of benefits to its employees.
Role Description SMBC Bank is seeking a highly skilled Vice President, Quantitative Credit Modeling to join our dynamic team in New York City. This role focuses on the quantitative model development, validation finding remediation, and maintenance of advanced credit risk models for wholesale and commercial portfolios, ensuring alignment with regulatory requirements (e.g., CCAR/DFAST, CECL, Basel III/IV) and business objectives. The ideal candidate will possess strong technical expertise in quantitative modeling, a deep understanding of regulatory frameworks, and the ability to collaborate across risk, finance, and technology teams.
Responsibilities
Design, develop, and calibrate credit risk models (PD, LGD, EAD) for wholesale/commercial/consumer portfolios, including stress testing (CCAR/DFAST), CECL, and Basel III/IV-compliant risk rating frameworks.
Lead end-to-end CCAR/DFAST stress testing processes, including data acquisition and processing, variable transformation, estimation, and calibration. Enhance existing models by implementing improvements based on back-testing outcomes, sensitivity analyses, and new data trends.
Support regulatory submissions and address feedback from examiners (e.g., FRB, OCC).
Regulatory Compliance & Stress Testing
Lead end-to-end execution of CCAR/DFAST stress testing processes, including scenario design, macroeconomic variable selection, and loss forecasting.
Ensure models comply with CECL accounting standards and Basel III/IV capital adequacy requirements.
Support regulatory submissions and address feedback from examiners (e.g., FRB, OCC).
Model Governance & Validation
Design and execute comprehensive back testing exercises to assess model performance under varying economic scenarios.
Collaborate closely with the model risk and model validation teams to ensure rigorous testing, well-documented methodologies, and compliance with internal and regulatory standards.
Perform ongoing model monitoring, back-testing, and sensitivity analysis to ensure robustness across economic cycles.
Document methodologies, assumptions, and limitations to meet audit and regulatory standards.
Cross-Functional Collaboration
Partner with credit officers, finance, and IT teams to operationalize models into underwriting, pricing, and capital planning workflows.
Present model results and strategic recommendations to senior management and risk committees.
Innovation & Industry Trends
Stay current with advancements in credit risk modeling (e.g., AI/ML applications, climate risk integration).
Qualifications & Skills
Education:
Master’s or PhD in Quantitative Finance, Statistics, Economics, Mathematics, or a related field.
Experience:
5+ years of experience in credit risk modeling, with a focus on wholesale or consumer portfolios. Proven track record in developing regulatory-compliant models (CCAR, CECL, Basel III/IV). Experience with large datasets and familiarity with banking products (e.g., corporate loans, CRE, leveraged finance).
Technical Skills:
Proficiency in Python programming languages and database tools (SQL, PySpark). Expertise in statistical methods: regression analysis, time series forecasting, machine learning (e.g., XGBoost, TensorFlow). Knowledge of credit risk platforms (e.g., Moody’s RiskCalc, CreditEdge) and cloud environments (AWS, Azure) preferred.
Certifications (Preferred):
FRM (Financial Risk Manager), CFA, or CRC (Credit Risk Certification).
Soft Skills:
Strong communication skills to translate technical concepts for non-technical stakeholders; ability to manage multiple priorities in a fast-paced regulatory environment.
Role Details
Seniority level:
Mid-Senior level
Employment type:
Full-time
Job function:
Product Management
Industries:
Financial Services and Banking
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The anticipated salary range for this role is between $155,000 and $195,000. The specific salary offered to an applicant will be based on their qualifications, experiences, and an analysis of the current compensation paid in their geography and the market for similar roles at the time of hire. The role may also be eligible for an annual discretionary incentive award. In addition to cash compensation, SMBC offers a competitive portfolio of benefits to its employees.
Role Description SMBC Bank is seeking a highly skilled Vice President, Quantitative Credit Modeling to join our dynamic team in New York City. This role focuses on the quantitative model development, validation finding remediation, and maintenance of advanced credit risk models for wholesale and commercial portfolios, ensuring alignment with regulatory requirements (e.g., CCAR/DFAST, CECL, Basel III/IV) and business objectives. The ideal candidate will possess strong technical expertise in quantitative modeling, a deep understanding of regulatory frameworks, and the ability to collaborate across risk, finance, and technology teams.
Responsibilities
Design, develop, and calibrate credit risk models (PD, LGD, EAD) for wholesale/commercial/consumer portfolios, including stress testing (CCAR/DFAST), CECL, and Basel III/IV-compliant risk rating frameworks.
Lead end-to-end CCAR/DFAST stress testing processes, including data acquisition and processing, variable transformation, estimation, and calibration. Enhance existing models by implementing improvements based on back-testing outcomes, sensitivity analyses, and new data trends.
Support regulatory submissions and address feedback from examiners (e.g., FRB, OCC).
Regulatory Compliance & Stress Testing
Lead end-to-end execution of CCAR/DFAST stress testing processes, including scenario design, macroeconomic variable selection, and loss forecasting.
Ensure models comply with CECL accounting standards and Basel III/IV capital adequacy requirements.
Support regulatory submissions and address feedback from examiners (e.g., FRB, OCC).
Model Governance & Validation
Design and execute comprehensive back testing exercises to assess model performance under varying economic scenarios.
Collaborate closely with the model risk and model validation teams to ensure rigorous testing, well-documented methodologies, and compliance with internal and regulatory standards.
Perform ongoing model monitoring, back-testing, and sensitivity analysis to ensure robustness across economic cycles.
Document methodologies, assumptions, and limitations to meet audit and regulatory standards.
Cross-Functional Collaboration
Partner with credit officers, finance, and IT teams to operationalize models into underwriting, pricing, and capital planning workflows.
Present model results and strategic recommendations to senior management and risk committees.
Innovation & Industry Trends
Stay current with advancements in credit risk modeling (e.g., AI/ML applications, climate risk integration).
Qualifications & Skills
Education:
Master’s or PhD in Quantitative Finance, Statistics, Economics, Mathematics, or a related field.
Experience:
5+ years of experience in credit risk modeling, with a focus on wholesale or consumer portfolios. Proven track record in developing regulatory-compliant models (CCAR, CECL, Basel III/IV). Experience with large datasets and familiarity with banking products (e.g., corporate loans, CRE, leveraged finance).
Technical Skills:
Proficiency in Python programming languages and database tools (SQL, PySpark). Expertise in statistical methods: regression analysis, time series forecasting, machine learning (e.g., XGBoost, TensorFlow). Knowledge of credit risk platforms (e.g., Moody’s RiskCalc, CreditEdge) and cloud environments (AWS, Azure) preferred.
Certifications (Preferred):
FRM (Financial Risk Manager), CFA, or CRC (Credit Risk Certification).
Soft Skills:
Strong communication skills to translate technical concepts for non-technical stakeholders; ability to manage multiple priorities in a fast-paced regulatory environment.
Role Details
Seniority level:
Mid-Senior level
Employment type:
Full-time
Job function:
Product Management
Industries:
Financial Services and Banking
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