Head of model risk

Talentnet Corporation; Kuala Lumpur; On-site; MYR 96,000 - 192,000; Full time

Job summary

Talentnet Corporation represents a leading financial institution in Vietnam seeking a Senior Risk Leader to oversee risk modeling, independent model validation and model risk management capabilities. You will strengthen the end-to-end model lifecycle, enhance quantitative risk capabilities and drive AI adoption within risk management.

You will report to senior leadership and manage a sizable team of quantitative risk and model validation professionals, guiding governance standards and regulatory

Qualifications

• Master's degree or higher in Statistics, Mathematics, Quantitative Finance, Economics, Data Science, Computer Science or a related field.
• 12+ years in banking risk, analytics, model development or validation with leadership experience.
• Strong expertise in credit risk modeling, model validation and model risk management.
• Knowledge of Basel II/III, IRB, IFRS 9, stress testing and portfolio analytics.
• Experience leading large-scale analytics, model governance or transformation initiatives.
• Familiarity with AI/ML model governance, explainability and MLOps.
• Experience with senior management, regulators, auditors and cross-functional stakeholders.
• Proficiency with SAS, SQL, Python or R and modern deployment environments.

Responsibilities

• Define and execute the strategy for risk modeling, validation and analytics across risk domains.
• Lead development and enhancement of credit risk, regulatory and portfolio models.
• Oversee validation of material models and establish model risk management governance.
• Promote automation, scalable analytics infrastructure and responsible AI practices.
• Provide senior oversight for regulatory and enterprise risk modeling (Basel, IFRS 9, stress testing).

Skills

Risk modeling

Model validation

Model governance

Leadership

Executive communication

Stakeholder management

MLOps

AI governance

SAS/SQL/Python/R

Education

Master's degree or higher in quantitative field

Tools

SAS

SQL

Python

R

Job description

Location: Hanoi, Vietnam

Our client is a leading financial institution in Vietnam undergoing significant transformation in risk management, advanced analytics and data-driven decision making.

The organization is seeking a senior risk leader to oversee its risk modeling, independent model validation and model risk management capabilities. The role will be responsible for strengthening the end-to-end model lifecycle, enhancing quantitative risk capabilities and supporting the adoption of advanced analytics and AI across risk management.

This is a senior leadership position reporting to the Chief Risk Officer / senior Risk Management leadership and leading a sizeable team of quantitative risk and model validation professionals.

Key Responsibilities

• Define and execute the overall strategy for risk modeling, model validation and advanced risk analytics.
• Lead the development and enhancement of credit risk, regulatory, portfolio and business decision models.
• Oversee models supporting areas such as credit decisioning, customer behavior, collections, early warning, portfolio management and other risk applications.
• Strengthen model development methodologies, documentation standards, monitoring practices and technical governance.
• Promote more efficient model development and deployment through automation, modern analytics infrastructure and MLOps practices.
• Lead the independent validation of material models across different risk areas.
• Establish and continuously enhance the organization's Model Risk Management framework and model lifecycle governance.
• Ensure effective challenge of model methodology, assumptions, data, implementation and performance.
• Oversee model inventory, risk classification, validation planning, performance monitoring, issue remediation and management reporting.
• Develop appropriate validation and governance standards for traditional statistical models as well as AI/ML-based models.
• Ensure model governance practices remain aligned with regulatory expectations and recognized industry standards.
• Provide senior oversight for regulatory and enterprise risk modeling, including areas such as Basel, IFRS 9, stress testing, capital adequacy and portfolio risk analytics.
• Work closely with senior management, regulators, auditors and relevant stakeholders on model-related matters.
• Support the organization's ongoing enhancement of quantitative risk management capabilities and regulatory readiness.

Advanced Analytics & AI

• Drive the responsible adoption of advanced analytics, machine learning and emerging AI technologies within risk management.
• Sponsor analytics initiatives across areas such as underwriting, fraud detection, collections, early warning and portfolio monitoring.
• Ensure appropriate governance, explainability, monitoring and human oversight for advanced models.
• Promote automation across model development, validation, deployment, monitoring and reporting processes.

People & Stakeholder Leadership

• Lead and develop a sizeable team covering model development and independent model validation.
• Build strong technical and leadership capabilities and establish succession plans for key positions.
• Partner closely with Risk, Business, Finance, Technology, Data, Operations and Internal Audit teams.
• Foster a culture of analytical rigor, independence, innovation and continuous improvement.

Requirements

• Master's degree or higher in Statistics, Mathematics, Quantitative Finance, Economics, Data Science, Computer Science, or another relevant quantitative discipline.
• Approximately 12+ years of relevant experience in banking risk management, quantitative analytics, model development or model validation, with substantial leadership experience.
• Strong expertise in credit risk modeling, model validation and model risk management.
• Solid knowledge of Basel II/III, IRB, IFRS 9, stress testing, capital adequacy and portfolio analytics.
• Experience leading large-scale quantitative risk, analytics, model governance or related transformation initiatives.
• Good understanding of AI/ML model governance, model explainability, monitoring and MLOps.
• Experience working with senior management, regulators, auditors and cross-functional stakeholders.
• Familiarity with analytical technologies such as SAS, SQL, Python or R and modern data/model deployment environments.
• Strong strategic leadership, stakeholder management and executive communication capabilities.
• Professional qualifications such as FRM, CFA, PRM or equivalent are advantageous.

Candidate Profile

The ideal candidate is an established quantitative risk leader who combines strong technical depth in risk modeling with leadership experience in model governance and independent validation.