Chapter Lead Data Science - Fraud, Scams & Cyber

Chapter Lead – Gen AI & Machine Learning, COO Data Science

• Scale advanced machine learning, Gen AI and agentic AI solutions across the Group

• Solve complex challenges to influence high-priority initiatives that protect millions of customers

• Lead modern data science, cloud and engineering tech teams to build and deploy safer production-ready AI.

Do work that matters

At CommBank, we are investing in AI to create better customer experiences and meaningful value for the communities we serve. Within COO Data Science, advanced data science, software engineering and AI platforms come together to solve complex problems at scale.

As Chapter Lead - Gen AI & Machine Learning, you will lead a specialist chapter of data scientists working across Generative AI, agentic AI and machine learning in the fraud and scams domain. You will shape technical direction, guide architecture and design decisions, strengthen delivery practices, and grow the capability of the team.

This is a hands-on leadership role. You will help move ideas from experimentation through evaluation, validation, deployment and ongoing monitoring, while ensuring solutions are safe, scalable and effective in a regulated environment.

See yourself in our team

You will join a highly technical data science team developing machine learning and emerging AI capabilities that help protect customers from fraud and scams. The team works closely with product, engineering, platform, architecture, MLOps and risk partners to deliver trusted solutions that operate reliably at scale.

We value curiosity, pragmatism, inclusion and continuous improvement. We share knowledge, test emerging methods thoughtfully and maintain a strong focus on customer outcomes, technical quality and responsible AI.

We support flexible ways of working, with a balance between time in the office and remote work. Talk to us about arrangements that work for you.

In this role, you will:

• Lead the design, development and implementation of machine learning, Generative AI and agentic AI solutions that support strategic business and customer outcomes.

• Apply statistical methods and structured problem solving to complex fraud and scams challenges.

• Progress solutions from exploration and experimentation through evaluation, validation, deployment and ongoing monitoring.

• Design and develop Generative AI capabilities, including Retrieval-Augmented Generation, orchestration, guardrails and agentic AI systems.

• Identify opportunities to expand the effective and responsible use of data science, machine learning and AI across fraud and scams.

• Partner with product, engineering, platform, architecture, MLOps, risk and business stakeholders to define problems, evaluate trade-offs and translate technical insights into action.

• Establish strong technical practices across data and feature engineering, model selection, evaluation, testing, documentation and production deployment.

• Lead and develop the chapter through coaching, knowledge sharing, technical guidance and collaboration.

We’re interested in hearing from people who:

• Are experienced data science or machine learning leaders that combine their strong technical judgement with a people-first approach to scale Agentic, ML and AI solutions.
• Enjoy solving complex problems, developing others and turning emerging technologies into reliable, practical outcomes.
• Communicate clearly, build trusted relationships and can operate effectively across technical, business and risk contexts.

You will bring:

• Proven experience leading teams or providing significant technical leadership across machine learning, Generative AI or agentic AI initiatives.

• A strong foundation in statistics, probability and machine learning, with the ability to interpret, evaluate and explain model behaviour.

• Hands-on experience designing, developing and evaluating production AI or machine learning solutions, including large language models, NLP or conversational AI.

• Advanced Python capability, together with experience using SQL and relevant machine learning or deep learning frameworks.

• Experience integrating AI systems with data pipelines, services and enterprise platforms, and working with MLOps teams and production delivery practices.

• Practical knowledge of cloud environments such as AWS or Azure, version control, continuous integration and delivery, containerization, monitoring and observability.

• Experience applying responsible AI, model governance, risk management and documentation requirements in a regulated or similarly complex environment.

• Strong written and verbal communication skills, including the ability to influence senior stakeholders and explain technical decisions to varied audiences.

• A tertiary qualification in Data Science, Statistics, Computer Science, Engineering, Mathematics or a related discipline is beneficial.

Working with us

At CommBank, we foster a culture of inclusion and respect, celebrating diversity across cultures, abilities, genders, gender expressions and sexual orientations. If this role excites you but you don’t meet every requirement, we still encourage you to apply as you may be the right fit for this role or another opportunity with us.

We’re committed to making a meaningful difference for Australia’s First Nations peoples and welcome all applicants.

Advertising End Date: 13/10/2026