Model Risk Senior Consultant

Summary:

The Model Risk Management Group (MRMG) is a centralized model risk management function within the Bank. It has seen fast growth in the past few years reflecting global regulators' increasing attention on model risk. We are searching for an Senior Consultant, Risk analytics to join our team. The primary responsibility of this role is to act as a lead contributor in the discovery and diagnostic of AI/ML model related risks including input data, assumption, conceptual soundness, methodology, outcomes analysis, benchmarking, monitoring and model implementation.

Specific Responsibilities

- Performs validation of Artificial Intelligence (AI), Machine Learning (ML) and Generative AI (GenAI) models

- Independently validate AI/ML and GenAI models across supervised, unsupervised, reinforcement learning and foundation model categories.

- Evaluate Gen AI model risk, including hallucination, prompt injection, data leakage, reproducibility, and alignment with Responsible AI principles.

- Assess model robustness, interpretability, fairness, and bias through quantitative and qualitative techniques.

- Has solid understanding of risks that are posed by AI/ML models (Fairness, Privacy, Transparency and Explainability, etc.)

- Has familiarity in financial models used in portfolio analysis, asset management, Value at Risk, Monte Carlo, CAPM, Factors.

- Has fair understanding of stress testing, CCAR, CECL, etc.

- Solves complex quantitative problems and takes a new perspective on existing solutions.

- Acts independently and analyzes possible solutions using technical experience and judgment and precedents.

- Develops and maintains an understanding of many algorithms across supervised learning, unsupervised learning and time series analysis.

- Utilizes expertise in machine learning algorithms and statistics to challenge how algorithms are selected, trained and tested.

- Also performs reviews of bank-wide quantitative models including models used for CECL and CCAR/DFAST stress testing, credit risk loss projections (PD, LGD, EAD), operational risk, interest rate risk models, AML (Anti-Money Laundering and Fraud Detection), and various machine learning models.

- Ensure model development, monitoring, and validation approaches meet regulatory expectations such as SR 11-7 and internal risk management needs.

- Evaluate conceptual soundness of model specifications; reasonableness of assumptions and reliability of inputs; completeness of testing performed to support the correctness of the implementation; robustness of numerical aspects; suitability and comprehensiveness of performance metrics and risk measures associated with model use.

- Review model documents, and conduct test runs on model codes.

- Assess and measure the potential impact of model limitations, parameter estimation, error and/or deviations from model assumptions; compare model outputs with empirical evidence and/or outputs from model benchmarks.

- Document and present observations to Model Validation Team Lead and to model owners and users, recommend remediation action plans, track remediation progress and evaluate remediation evidence.

- Monitor model performance reports on an on-going basis to ensure models remain valid, as well as contribute in the bank-wide model risk and control assessment.

- Support development of comprehensive documentation and testing of risk management framework. Deliver a work product that requires little revision.

- Establish and maintain strong relationship with key functional stakeholders such as model developers, model owners, and users.

Qualifications - External

- 6 to 10 years of modeling or quantitative analysis experience, preferably in a discipline relevant to risk management to include statistical/mathematical, AI/ML and financial modeling.

- A College or University degree in STEM field, mathematics, actuarial science, engineering or statistics or related discipline (Advanced degree preferred).

- Strong knowledge of AI/ML techniques including classification and 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; Familiarity with GenAI risks and controls: hallucination detection, retrieval-augments-generation (RAG), prompt engineering, etc.

- 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 as well as Word and PowerPoint is required.

- Deep understanding of linear regression and logistic regression.

- Familiarity with cloud data technologies is desired.