Head of Monitoring, Governance & Control – GCB Unsecured Ris…

The Head of Monitoring, Governance & Control – GCB Unsecured Risk Models & USCB Unsecured CCAR/CECL reports to the GCB Risk Model Utility Head of GCB Unsecured Modeling and US Unsecured CCAR/CECL (C16) and leads monitoring, reporting, and governance/control activities across 400+ risk models responsible for booking ~7MM accounts annually, managing ~40MM customers and the proper regulatory reporting of $140B+ in assets. Supported by an on-shore and off-shore staff consisting of ~20 statisticians and data analysts, this role works across the GCB Risk Modeling Utility Leadership Team to ensure all risk models are compliant with regulatory (OCC, FRB, CFPF) and Citi policy requirements and properly monitored to ensure all customers decisions continue to satisfy risk appetite framework requirements.

Responsibilities:

Risk Modeling Unit responsibilities:

• Ensure required controls are in places (e.g. EUC and MCA), artifacts evidenced and reported on at a utility level

• Ensure that outstanding issues (e.g. MRM Limitations, CAPS, or regulatory findings) are identified tracked and reported on at utility level

• Own utility wide policies and procedures; coordinate with modeling teams to ensure team specific procedures are consistent with utility wide policy and procedures; manage repository containing utility wide and team specific policies and procedures

Unsecured Risk Models and USCB CCAR/CECL Models responsibilities:

• Validate statistical as well as non-statistical models to improve credit card risk management, such as scorecards for predicting risk behavior spanning all stages of the consumer credit cycle, non-scoring models used in various underwriting strategies and loss forecasting models. Perform statistical analyses to understand performance trends of all models and develop diagnostic functions to validate statistical models for financial and risk forecasting and analysis. Ensure on-time and quality submissions of Ongoing Performance Assessments (OPAs), Annual Model Reviews (AMRs), and Model Change Addendums (MCAs) for all models in responsiblity scope

• Lead engagement with business-line, risk, and independent risk stakeholders to communicate on-going monitoring results highlighting risk associated with model deterioration. Use statistical modeling expertise and analytical skills to provide meaningful insights on model performance and recommendations on ongoing usage of these models.

• Ensure that the development and validation of statistical financial models is conducted in compliance with all Global Risk and external regulatory guidelines. Robust engagement with Independent Risk, Model Risk Management, and other 2nd Line of Defense teams to ensure compliance

• Deploy automated and efficient model monitoring capabilities for all risk models in scope yielding timely, robust, and flexible reporting capabilities.

• Constructive engagement with regulators (OCC, Fed, etc.) and Internal Audit to evidence transparent and robust compliance with external rules and internal policies

• Lead direct reports with responsibility for hiring, performance management, and planning

Qualifications:

• 11+ years experience
• Demonstrated leaderships in performing quantitative analysis, statistical modeling, loss forecasting, loan loss reserve modeling, and/or stress testing (e.g., CCAR/DFAST).
• Demonstrated experience managing quantitative analytic teams including off-shore capabilities
• Ability to manage work and relationships in cross functional teams, including country/region’s business stakeholders, model validation and governance teams, and other global functions
• Experience with predictive modeling for financial services products for retail consumer businesses
• Strong communication skills required to translate model design, specification and performance details to technical and non-technical audiences.

Education:

• Master’s degree (preferably a PhD), in Statistics, Applied Mathematics, Operations Research, Economics, or other highly quantitative discipline

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