Model Risk Management Specialist 

The   Model Risk Management (MRM) Specialist   operates   within the Second Line of Defence (2LoD) to manage,   validate , and mitigate risks arising from the bank's mathematical, statistical, and Artificial Intelligence (AI/ML) models. This role focuses on ensuring that traditional quantitative models (e.g., credit risk, stress testing) and advanced AI systems (e.g., Generative AI, LLMs, machine learning credit scoring) are conceptually sound, compliant with regulations, and governed ethically.

This position reports directly to the   Chief Risk Officer .

Key Responsibilities

Model Validation & Conceptual Review

- Technical Validation:   Conduct independent technical validations of high-risk models, including credit risk   models, Expected Credit Loss (MFRS9)   models   ,   market risk, AML transaction monitoring, and financial forecasting systems.

- AI & Machine Learning Assessment:   Review and challenge advanced AI algorithms, including deep learning, NLP, and Generative AI models. Assess data lineage, hyperparameter tuning, and training   methodology .

- Explainability & Bias Mitigation:   Evaluate AI models for explainability (XAI), interpretability, algorithmic bias, data drift, and ethical implications. Ensure models do not produce discriminatory outcomes.

Governance, Inventory & Documentation

- Model Inventory Control:   Maintain   and update the comprehensive Bank-Wide Model Inventory, ensuring all traditional and AI/ML models are properly tiered by risk level.

- Documentation Standards:   Produce comprehensive validation reports documenting methodology, findings, and recommendations in line with the Bank's validation standards; present findings to the model development team and escalate material issues   to CRO and RMC, BRMC;   Assist in preparing model risk governance materials for the Risk Management Committee, including validation summaries and model risk status updates

- Performance Monitoring:   Establish   metrics to   monitor   ongoing model performance, data drift, and model decay, triggering re-validation or remediation when thresholds are breached.

- Support the model risk governance process by tracking open findings and action plans, following up with model owners on remediation timelines, and flagging overdue items.

Regulatory Compliance & Strategic Risk

- Framework Alignment:   Ensure the bank’s model inventory   complies with   global and regional regulatory expectations, including   Bank Negara Malaysia (BNM) frameworks   (e.g., Credit Risk, Risk Governance Policy Documents) ,   MFRS ,    and   emerging   AI Governance Frameworks .

- Effective Challenge:   Deliver an independent, constructive, and rigorous "effective challenge" to data scientists, quantitative developers, and First Line (1LoD) business owners   regarding   model limitations and risks.

- Committee Reporting:   Prepare clear validation reports and synthesize complex technical vulnerabilities into executive summaries for the Risk Committee (MRC) and Chief Risk Officer (CRO).

Required Skills & Qualifications

Education & Experience

- Education:   Master’s degree or PhD in a highly quantitative field such as Statistics, Mathematics, Financial Engineering, Data Science, Computer Science, or Econometrics.

- Experience:   >5   years of experience   in model validation, quantitative risk management, or advanced data science within a banking, financial institution, or financial consulting environment.

Technical & Core Competencies

- Programming Proficiency:   Advanced hands-on coding skills in   Python, R, SQL, or SAS   for data manipulation and statistical replication.

- AI/ML Frameworks:   Practical familiarity with machine learning libraries and frameworks (e.g., Scikit-learn, TensorFlow,   PyTorch ) and automated validation tools.

- Banking Domain Knowledge:   Deep comprehension of financial products, risk metrics (PD, LGD, EAD), stress testing methodologies, and economic capital   modeling .

- Communication:   Ability to articulate complex mathematical and algorithmic concepts clearly to non-technical business stakeholders and senior executives
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