AVP, Model Monitoring and Analytics Data Scientist (SAS, R, Python) - Hybrid

Key Skills

Data Analytics, Data Modeling, Data science techniques, Machine learning techniques, MATLAB Programming, Python Programming, R Programming, SAS, SQL, Tableau

Job Description

• Manage model risk across the model life-cycle including model validation, ongoing performance evaluation and annual model reviews.
• Perform statistical analysis, and monitor performance of consumer valuation models
• Perform deep dive and/or root cause analysis, generate insights on model performance and provide recommendations to model developers and/or model users•
• Conduct analysis and packages it into detailed technical documentation report for validation purposes sufficient to meet regulatory guidelines and exceed industry standards.
• Documentation of required control (e.g. EUC and MCA/ARCM) 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
• Manage stakeholder interaction with model developers and business owners during the model life-cycle.
• Communicate results to diverse audiences.
• Assess and quantify model risk due to model limitations to inform stakeholders of their risk profile and development of compensating controls.

Unsecured Risk Models responsibilities:

• Build automated SAS codes to calculate validation metrics that are in line with metrics defined by the Model Risk Management team
• Generation of quarterly model performance monitoring reports, identifying performance breaches, performance of deep dive analysis and generate insights
• Use statistical and non-statistical modeling expertise and analytical skills to provide meaningful insights on model performance and recommendations regarding ongoing usage of these models
• Apply understanding of the mathematical principles of credit risk models in order to write logic to validate and implement the model, and then assess the immediate model outcome for accuracy and separately evaluate the results.
• Communicate insights and observations to model developers and model users with recommendations to remediate breaches
• Coordinate with Independent Risk, Model Risk Management, and other 2nd Line of Defense teams to communicate on-going monitoring results highlighting risks associated with model deterioration as well as ensure models are developed and used in compliance with Global Risk and external regulatory guidelines.
• Ensure on-time and quality submissions of Ongoing Performance Assessments, Annual Model Reviews, Model Changes, Limitations & Compensating Controls for all models in responsibility scope through the Model Risk Management tracking platform.
• Assist with coordinated engagement of regulators (OCC, Fed, etc.) and Internal Audit to evidence transparent and robust compliance with external rules and internal policies.
• Engage with business risk team to complete regular risk and control monitoring assessments.
• Document and assist to coordinate with Independent Risk, Model Risk Management, and other 2nd Line of Defense teams to communicate on-going monitoring results highlighting risks associated with model deterioration as well as ensure models are developed and used in compliance with Global Risk and external regulatory guidelines.

Qualifications:

• 5+ years relevant experience in an analytical capacity required
• Advanced SAS/SQL, Tableau, MATLAB, Python, R or C programming in UNIX
• Sound knowledge of statistical and non-statistical modeling concepts (such as scorecards for predicting risk behavior, and non-scoring models used in underwriting strategies) as well as Cards business/risk management concepts (such as basic P&Ls, Strategic Plan, CCAR/CECL and Risk Appetite Framework) and other industry best practices
• Ability to perform quantitative analysis and modeling for decision models, policy strategies, loss forecasting, loan loss reserve modeling, and/or stress testing (e.g., CCAR/DFAST) using a variety of techniques (linear/ logistic regression, segmentation, decision tree, machine learning, time series, and optimization)
• Drive effective performance tracking and adherence to internal policies and regulatory requirements including performance implications of such policy/actions
• Identify enhancement opportunities and more granular insights that can be acted upon
• Build presentations to effective communicate technical information to a wide audience
• Ability to successfully engage cross functionally - both projects and teams, including country/region’s business stakeholders, model validation and governance teams, and other global function

Education:

• Minimum Requirement: Bachelor’s Degree in statistics, mathematics, engineering, physics, economics, or related quantitative discipline.