Center Head of Modelling & Model Validation

**ACG_3829_JOB
Our client is a leading bank in Vietnam who is looking for a qualified candidate to join their firm.Strategic Leadership & Governance**

• Define and execute the modeling, model validation, and risk analytics strategy in alignment with the Bank's strategic priorities, AI-enabled risk transformation agenda, and Basel roadmap.

• Establish and maintain the Group Model Risk Management Framework and enterprise-wide model lifecycle governance, including model inventory, model risk classification, model risk assessment, tiering, approval, implementation, monitoring, change control, retirement, and consolidated model risk reporting.

• Represent RMD in model governance committees and engagements with SBV, auditors, and other relevant stakeholders.

• Ensure alignment of modeling, validation, and risk analytics capabilities across the Bank and its subsidiaries, with clear ownership and accountability.

Oversight of Model Development Department

• Provide strategic direction for the development, enhancement, implementation, and monitoring of risk, regulatory, and business models across the Group.

• Oversee credit risk models, including application, behavioral, collection, attrition, cross-sell, propensity, and early-warning models; Basel II/III/IV, IFRS 9, stress testing, capital and portfolio analytics models; and advanced AI/ML, fraud analytics, and alternative-data models.

• Establish modern development methodologies, coding and documentation standards, reusable assets, and common feature libraries.

• Drive industrialized model development and deployment through MLOps, automated pipelines, CI/CD, controlled retraining, and performance monitoring, in coordination with Business, IT, and EDA.

Oversight of Model Validation Department

• Ensure independent, risk-based, and timely validation of all material models across the Group in accordance with the Model Validation Framework.

• Approve the annual validation plan and oversee validation of credit, market, liquidity, operational, regulatory, and business models.

• Establish validation standards for AI/ML models, including explainability, stability, data integrity, bias and fairness where applicable, implementation accuracy, and ongoing monitoring.

• Ensure effective challenge of model methodology, assumptions, data, implementation, and performance; track findings and remediation to closure; and provide enterprise-wide reporting on model inventory, model risk classification, risk assessments, lifecycle status, and material model-risk exposures.

• Embed model-risk controls into MLOps, including end-to-end lineage, versioning, dev-to-production consistency, tiered change control, retraining guardrails, and continuous evidence for audit and review.