Senior Consultant - Model Risk - AI/ML

You will be joining the Model Risk Management Group (MRMG) as a Senior Consultant, Risk Analytics. Your primary responsibility will be to lead the discovery and diagnostic of AI/ML model related risks, including input data, assumption, conceptual soundness, methodology, outcomes analysis, benchmarking, monitoring, and model implementation.

**Key Responsibilities:**

• Perform validation of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) models.
• Independently validate AI/ML and GenAI models across different categories.
• Evaluate Gen AI model risk factors such as hallucination, prompt injection, data leakage, reproducibility, and alignment with Responsible AI principles.
• Assess model robustness, interpretability, fairness, and bias through quantitative and qualitative techniques.
• Understand risks posed by AI/ML models, including Fairness, Privacy, Transparency, and Explainability.
• Have familiarity with financial models used in portfolio analysis, asset management, Value at Risk, Monte Carlo, CAPM, and Factors.
• Develop and maintain an understanding of various algorithms across supervised learning, unsupervised learning, and time series analysis.
• Utilize expertise in machine learning algorithms and statistics to challenge the selection, training, and testing of algorithms.
• Review and validate bank-wide quantitative models including CECL, CCAR/DFAST stress testing, credit risk loss projections, operational risk, interest rate risk models, AML, and various machine learning models.
• Ensure model development, monitoring, and validation approaches meet regulatory expectations and internal risk management needs.
• Document and present observations to the Model Validation Team Lead and model owners and users, recommend action plans, track remediation progress, and evaluate evidence.
• Establish and maintain strong relationships with key stakeholders such as model developers, owners, and users.

**Qualifications:**

• 6 to 10 years of modeling or quantitative analysis experience, preferably in risk management, statistical/mathematical, AI/ML, and financial modeling.
• College or University degree in STEM field, mathematics, actuarial science, engineering, statistics, or related discipline (Advanced degree preferred).
• Strong knowledge of AI/ML techniques including classification, clustering, gradient boosting, neural networks, NLP models, and foundational models like GPT, BERT, etc.
• Experience in validating machine learning models for performance, fairness, explainability, and compliance.
• Good interpersonal, verbal, and written communication skills.
• Programming experience in Python required, experience in SAS and R desired.
• Mastery of analytical tools such as Excel, Word, and PowerPoint.
• Deep understanding of linear regression and logistic regression.
• Familiarity with cloud data technologies is desired. You will be joining the Model Risk Management Group (MRMG) as a Senior Consultant, Risk Analytics. Your primary responsibility will be to lead the discovery and diagnostic of AI/ML model related risks, including input data, assumption, conceptual soundness, methodology, outcomes analysis, benchmarking, monitoring, and model implementation.

**Key Responsibilities:**

• Perform validation of Artificial Intelligence (AI), Machine Learning (ML), and Generative AI (GenAI) models.
• Independently validate AI/ML and GenAI models across different categories.
• Evaluate Gen AI model risk factors such as hallucination, prompt injection, data leakage, reproducibility, and alignment with Responsible AI principles.
• Assess model robustness, interpretability, fairness, and bias through quantitative and qualitative techniques.
• Understand risks posed by AI/ML models, including Fairness, Privacy, Transparency, and Explainability.
• Have familiarity with financial models used in portfolio analysis, asset management, Value at Risk, Monte Carlo, CAPM, and Factors.
• Develop and maintain an understanding of various algorithms across supervised learning, unsupervised learning, and time series analysis.
• Utilize expertise in machine learning algorithms and statistics to challenge the selection, training, and testing of algorithms.
• Review and validate bank-wide quantitative models including CECL, CCAR/DFAST stress testing, credit risk loss projections, operational risk, interest rate risk models, AML, and various machine learning models.
• Ensure model development, monitoring, and validation approaches meet regulatory expectations and internal risk management needs.
• Document and present observations to the Model Validation Team Lead and model owners and users, recommend action plans, track remediation progress, and evaluate evidence.
• Establish and maintain strong relationships with key stakeholders such as model developers, owners, and users.

**Qualifications:**

• 6 to 10 years of modeling or quantitative analysis experience, preferably in risk management, statistical/mathem