• Gain exposure to a wide range of stakeholders across the bank  

• Shape the future of Model Risk Management at NAB 

• Join a supportive and collaborative team in this high-profile role 

Our people are customer obsessed. They prioritise the needs and satisfaction of the customer above all else.  Our mindset fosters innovation and creates strong, lasting customer relationships as we strive to be the most customer centric company in Australia and New Zealand.  

As the Head of Model Risk, you will join the Risk Frameworks, Execution and Models Team that is doubling down on shaping our strategic Model Risk Management approach while protecting our customers. 

Each day, you’ll go above and beyond to:  

• Own enterprise model risk framework, policy/standards, and model risk appetite. 

• Lead independent validation strategy, methodology, and support annual coverage planning. 

• Approve validation conclusions and ensure quality and consistency of reports. 

• Provide credible challenge to model owners and senior stakeholders. 

• Maintain model inventory completeness, tiering, and lifecycle governance. 

• Monitor model risk posture through KRIs/KPIs and control effectiveness testing. 

• Deliver Board/ExCo reporting on model risk, issues, trends, and remediation. 

• Drive uplift roadmap (capability, tooling, monitoring automation, governance). 

• Oversee model incidents/breaches and ensure robust remediation and lessons learned. 

• Build and lead a high-performing model risk team, uplift enterprise capability. 

We’re looking for the best and brightest to deliver the best for our customers. You’ll need:  

• Extensive experience in banking/financial services, risk, analytics, and/or model development/validation.  

• Strong knowledge and experience across model governance/validation/model risk roles will be highly regarded.  

• Strong engagement and influencing skills with the ability to work closely with multiple stakeholders, to convey complex ideas clearly and concisely and deliver outcomes.  

• Strong understanding of how models affect e.g.: credit decisions and capital (IRB where relevant); provisioning (IFRS9/ECL drivers); stress testing and scenario analysis; pricing and customer decision; financial crime/fraud detection systems; remediation streams; and markets and treasury models. 

• AI/ML awareness including an understanding of the regulatory environment relating to the use of AI to challenge the development and implementation of AI/GenAI/Agentic models (vendor or “in-house” models) for a variety of use.