Model Risk Management Specialist

Ryt Bank; Kuala Lumpur; On-site; MYR 180,000 - 270,000; Full time

Job summary

Ryt Bank in Kuala Lumpur seeks a Model Risk Management Specialist to join the Second Line of Defence and oversee validation of traditional and AI/ML models.

You will conduct independent technical validations, assess AI methodologies, manage documentation, and report findings to the CRO and Risk Committee, ensuring ethical and regulatory compliance.

Qualifications

• Master’s or PhD in statistics, mathematics, financial engineering, data science, CS or econometrics.
• More than 5 years in model validation or quantitative risk in finance.
• Experience with AI/ML models and governance frameworks.

Responsibilities

• Perform independent technical validations of high‑risk models in credit risk, market risk and forecasting.
• Assess AI/ML methods, data lineage, hyperparameters, and training approaches.
• Evaluate explainability and bias, and ensure ethical, compliant deployment.

Skills

Python

R

SQL

SAS

ML Frameworks

Scikit-learn

TensorFlow

PyTorch

Communication

Banking domain knowledge

Education

Master’s degree / PhD in quantitative field

Tools

Scikit-learn

TensorFlow

PyTorch

Job description

TheModel Risk Management (MRM) Specialistoperateswithin 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 riskmodels, 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 trainingmethodology.

• 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: Maintainand 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 upscale material issuesto 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: Establishmetrics tomonitorongoing 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 inventorycomplies withglobal and regional regulatory expectations, includingBank Negara Malaysia (BNM) frameworks(e.g., Credit Risk, Risk Governance Policy Documents), MFRS ,andemerging AI Governance Frameworks.

• Effective Challenge: Deliver an independent, constructive, and rigorous “effective challenge” to data scientists, quantitative developers, and First Line (1LoD) business ownersregardingmodel 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: >5years 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 capitalmodeling.

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

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