The Model Risk Management (MRM) provides oversight for the MRM Framework, which consists of the policy, processes, and procedures. The Lead Quant Analyst - Model Risk Management (MRM) will be responsible for conducting independent model validations either developed in-house or by vendors to ensure models align with business objectives and design objectives. Verify that models are performing as expected, identify potential limitations, which includes assessing potential impact. Perform annual reviews and review the on-going monitoring reports to ensure that the models are performing as intended.
Key Deliverables (Duties and Responsibilities)
- Comprehensive Model Validation: Conduct thorough and comprehensive validations of various model components, ensuring that they are accurate, reliable, and aligned with the intended business objectives and regulatory requirements. This includes but is not limited to:
- Model Inputs Analysis: Apply data analysis techniques to assess the quality, integrity, and appropriateness of data used in the models. Examine data extraction, cleaning, transformation processes, and evaluate data-related assumptions and limitations.
- Model Conceptual Evaluation: Scrutinize the model design and construction, verifying the suitability of the modeling framework and theory for the intended use. Review model segmentation, variable selection, model testing procedures, and evaluation model assumptions, limitations, and risks.
- Model Code Review and Replication: Review model code to ensure correctness, accuracy, and absence of material errors. Collaborate with model developers to address any identified issues.
- Outcomes Analysis: Assess both in-sample and out-of-sample back test results; evaluate sensitivity and scenario testing, stress testing, benchmark model development, and quantitative and business performance metrics.
- Challenger Model Development (if required)
- Risk Identification and Mitigation: Provide effective challenges and identify potential model risks. Recommend appropriate mitigation measures and enhancements to improve model quality and compliance with regulatory standards.
- Ongoing Monitoring and Outcome Analysis: Assess the mechanisms for ongoing model performance monitoring, issue identification, and risk management to ensure effective oversight, policy compliance. Ensure that the model remains reliable, relevant, and compliant throughout its lifecycle, this includes review risk mitigation measures, compensating controls, and risk acceptances.
- Documentation and Reporting: Produce high-quality, comprehensive validation reports that clearly communicate findings, recommendations, and potential risks to both technical and non-technical stakeholders. Ensure that validation documentation adheres to internal standards and regulatory requirements (SR11-7).
- Review and upgradation of MRM policies and Procedures annually, with new trends
- Audit and Regulatory Review Support: Assist in gathering and providing materials requested by internal audit and regulators, drafting responses to questions, and defending validations in exams.
- Continuous Learning and Improvement: Stay up to date with emerging trends and best practices in model validation and regulatory requirements. Contribute to the enhancement of the model validation framework by suggesting process improvements and implementing industry-leading methodologies.
- Develop and maintain model governance documentation and performance monitoring, including periodically presenting results to MRM stakeholders.
- Interact with all key stakeholders including model users, model owners, vendors, Model Risk Governance, and other Validators throughout the model lifecycle including validation, ongoing performance evaluation.
- Build relationships across model owners, data scientists, Model Risk Management, Audit, and third-party vendors.
- Use subject matter expertise and analytics to proactively identify and address gaps and identify emerging risks.
- Contribute towards remediation efforts for model-related issues identified through exams, audits, and model validations.
- Advanced degree in Statistics, Mathematics, Economics, or related field
- Knowledge of E-23, FED SR11-7/ 26-2, OCC, CFPB, regulatory requirement (Must)
- Experience in validating in-house and vendor (third-party) statistical, qualitative or AI/ML models.
- Experience in using statistical tools like SAS, Python and R.
- Experience using Excel, SQL
- Excellent problem-solving skills, and attention to detail.
- Excellent report writing and communication skills.
- Stakeholder management skills, e.g., effective forward-looking planning, communication, and delivery of services.
- Proficiency in statistical methods (e.g., linear regression, logistic regression, survival analysis, ARIMA and other advanced ML methods)
- 5+ years of experience in model development, model validation, or model implementation within the financial industry.
- Knowledge of financial services/ banking domain.
- Strong understanding of model development and validation testing techniques
- Understanding banking, Financial crime & compliance and market products, risk methodologies, practices and procedures.
