Senior Advisor - Model Risk Management

Job Description

Candidate will be responsible for supporting all aspects of the global Model Risk Management (MRM) program. The successful candidate will support the global MRM efforts to ensure that models are appropriately designed, implemented, and managed including but not limited to conceptual soundness, model implementation and use, model documentation, model performance monitoring, governance, tuning and validation. Job Details Position Title: Senior Advisor - Model Risk Management Career Level: P2 Role Type: Hybrid Job Location: Bangalore About the Team: The Model Risk Management (MRM) provides oversight for the MRM Framework, which consists of the policy, processes, and procedures. The Senior Advisor - Model Risk Management (MRM) will be responsible to conduct independent model validations 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. Impact The Model Risk Management (MRM) provides oversight for the MRM Framework, which consists of the policy, processes, and procedures. The Senior Advisor - Model Risk Management (MRM) will be responsible to conduct independent model validations 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. Candidate will be based out of First Citizens India (FCI) office in Bangalore, report into the Associate Director of Model Risk Management, India, and function as an integral member of the global MRM organization. The role will work collaboratively with the global MRM team and act as their point of contact in India. Key Deliverables 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 Framework 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: 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. 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. 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. 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. Skills and Qualification Skills: Strong understanding of regulatory requirements, particularly those related to stress testing and capital planning: SR 11-7, OCC 2011-12. Experience in using statistical tools like Python and R. Experience using SQL to pull data from enterprise warehouse databases. Excellent problem-solving skills, and attention to detail. Excellent written and verbal communication skills. Strategic planning skills supported with analytical skills. Stakeholder management skills, e.g., effective forward-looking planning, communication, and delivery of services. Able to effectively manage tasks as agreed. Efficient and well-organized workflow and process design skills. Attention to detail, accuracy of information. Ability to effectively collaborate with team members. Education: Relevant Bachelor/ Master s degree, with preference for specialization in Statistics, Mathematics, Economics, or other quantitative discipline. Microsoft Office (Word, Excel, PowerPoint, and Outlook). Experience: 2+ years of experience in model development, model validation, or model implementation within the financial industry; Proficiency in statistical methods (e.g., linear regression, logistic regression, survival analysis, ARIMA) and programming languages (e.g., SAS, Python, R, SQL). Knowledge of financial services/ banking domain. Knowledge of statistical and machine learning models. Strong understanding of model development and validation testing techniques. Relationships & Collaboration 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. Accessibility Needs We are committed to providing an inclusive and accessible hiring process. If you require accommodations at any stage (e.g. application, interviews, onboarding) please let us know, and we will work with you to ensure a seamless experience.