Background information about the job or company

Work in a collaborative environment with highly talented and motivated colleagues who are passionate about winning together

Drive data‑led fraud outcomes through advanced analytics and modelling

Make a real difference through the work you do for our customers and communities.

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.

In the role of a Data Consultant, you will join the Fraud Prevention & Detection team that’s doubling down on protecting the community and our customers.

Responsibilities or duties

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

• Analyse data to identify fraud patterns, behaviours and emerging risks
• Develop, test and refine rules, models and analytical approaches
• Support deployment of analytics into operational systems and controls
• Monitor model and rule performance and identify uplift opportunities
• Work with Fraud Operations, Technology and Risk to deliver outcomes
• Apply fraud intelligence and typologies to improve approaches
• Communicate insights clearly to stakeholders
• Ensure alignment with risk, governance and regulatory requirements

Qualifications or requirements

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

• Strong data analytics capability, applying statistical techniques to fraud use cases
• Understanding of fraud typologies, behaviours and risk across customer journeys
• Extensive data science / analytics in financial services (fraud focus)
• Proven delivery of models at scale with measurable outcomes
• Strong technical capability across modelling and analytics tools
• Ability to translate data into practical insights for prevention and detection outcomes
• Experience working with large datasets to identify patterns and trends
• Stakeholder engagement across Fraud Operations, Technology and Risk

Any other provided details

A diverse and inclusive workplace works better for everyone

We know that our people make us who we are. That’s why we have built a culture of equity and respect - where everyone feels valued and appreciated for being their authentic selves. In partnership with our multiple Employee Resource Groups (ERGs), we continue to foster an inclusive environment, where all NAB colleagues’ unique backgrounds and identities are understood, respected and celebrated. We are committed to providing an environment where you can work your way.

• Analyse data to identify fraud patterns, behaviours and emerging risks
• Develop, test and refine rules, models and analytical approaches
• Support deployment of analytics into operational systems and controls
• Monitor model and rule performance and identify uplift opportunities
• Work with Fraud Operations, Technology and Risk to deliver outcomes
• Apply fraud intelligence and typologies to improve approaches
• Communicate insights clearly to stakeholders
• Ensure alignment with risk, governance and regulatory requirements

• Strong data analytics capability, applying statistical techniques to fraud use cases
• Understanding of fraud typologies, behaviours and risk across customer journeys
• Extensive data science / analytics in financial services (fraud focus)
• Proven delivery of models at scale with measurable outcomes
• Strong technical capability across modelling and analytics tools
• Ability to translate data into practical insights for prevention and detection outcomes
• Experience working with large datasets to identify patterns and trends
• Stakeholder engagement across Fraud Operations, Technology and Risk

• Strong data analytics capability, applying statistical techniques to fraud use cases
• Understanding of fraud typologies, behaviours and risk across customer journeys
• Extensive data science / analytics in financial services (fraud focus)
• Proven delivery of models at scale with measurable outcomes
• Strong technical capability across modelling and analytics tools
• Ability to translate data into practical insights for prevention and detection outcomes
• Experience working with large datasets to identify patterns and trends
• Stakeholder engagement across Fraud Operations, Technology and Risk