Credit Risk Modeler #980954Dexian • Dallas, TX, United States
Credit Risk Modeler #980954
Dexian • Dallas, TX, United States
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job_description.job_card.job_descriptionAnalyze commercial loan, borrower, and portfolio data to identify trends and emerging risks. Develop comprehensive model documentation, technical specifications, validation support materials, and presentations for internal stakeholders and regulators. Maintain accurate version control, audit trails, and documentation for all model changes and implementations. Partner with Credit, Lending, Finance, Data Teams, and Risk Management to integrate model outputs into decision-making processes. Support CECL model development, implementation, and ongoing analytics across balance-sheet portfolios. Collaborate with business units to understand commercial structures, risk frameworks, and modeling needs. Assist in regulatory exams, internal audit reviews, and model governance activities. Manage multiple concurrent workstreams and deadlines across modeling, documentation, and stakeholder requests. Participate in planning, implementation, and execution of internal analytics initiatives and model deployment. Work with business partners to translate technical modeling insights into actionable recommendations. Strong knowledge of CECL, Basel III risk frameworks, and commercial lending risk metrics (e.g., LTV, Borrowing Base Utilization, Hedge Effectiveness). Solid understanding of financial statements, commercial credit structures, and liquidity impacts. Technical proficiency in Python, SQL, R, SAS , and experience with model development and documentation . Hands-on experience with Moody’s RiskCalc or comparable risk modeling tools. Working knowledge of Jira or similar tools for workflow and documentation management. Ability to simplify quantitative concepts for business audiences and senior leadership. Strong analytical, problem-solving, and critical-thinking skills. Ability to operate in fast-moving environments, handle changing priorities, and work cross-functionally. Outstanding communication skills, both written and verbal. High attention to detail and strong organizational capabilities. Bachelor’s degree in Quantitative Finance, Mathematics, Statistics, Economics, or a related field (Master’s preferred). 4+ years of credit risk modeling experience within a commercial or institutional banking environment. 2+ years of experience using Moody’s RiskCalc or similar modeling tools. 1+ year leading projects or workstreams preferred. Position requires use of standard business technology and computer equipment. Relocation assistance available for the right candidate.
W-2 only (client cannot sponsor at anytime)
Credit Risk Modeler – Corporate & Institutional Banking (CIB)
Location : Onsite 4 days / week
Relocation : Available for qualified candidates
Position Overview
The Credit Risk Modeler is responsible for designing, developing, and maintaining credit risk models that support Corporate & Institutional Banking (CIB) portfolios. This role supports commercial lending risk management by building models, analyzing portfolio data, ensuring regulatory compliance, and partnering with cross-functional teams to integrate model insights into business decision-making.
The ideal candidate brings strong quantitative skills, experience with CECL and commercial credit modeling, and a solid understanding of financial statements, balance sheet impacts, and commercial markets.
Key Responsibilities
Model Development & Maintenance
- Design, develop, and maintain credit risk models including PD, LGD, and EAD for CIB portfolios.
- Build and enhance vendor-based models leveraging tools such as Moody’s RiskCalc or similar platforms.
- Conduct model monitoring, benchmarking, back-testing, and performance assessments.
- Ensure model transparency, interpretability, and compliance with model governance standards (e.g., SR 11-7, OCC 2011-12).
Portfolio Analytics & Documentation
Collaboration & Cross-Functional Support
Operational Responsibilities
Required Skills & Expertise
Qualifications
Additional Information
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