Chapter Lead Data Science - Fraud, Scams & Cyber

CommBank; Sydney; Hybride; AUD 250 000 - 350 000; Plein temps

Avantages offerts par ce poste

Flexible work arrangements

Résumé du poste

CommBank in Australia is seeking a Chapter Lead Data Science to steer GenAI, agentic AI and ML initiatives across fraud and scams. You will lead a high‑impact data science chapter, shaping architecture, setting delivery standards and coaching team members to build safe, production‑ready AI capable of operating in a regulated environment.

You will partner with product, engineering, platform and risk teams to translate complex technical insights into measurable business outcomes, while advancing

Qualifications

• Experience leading ML/GenAI initiatives.
• Strong foundation in statistics and ML concepts.
• Hands-on production AI experience with LLMs/NLP.
• Advanced Python, SQL and ML frameworks.
• Cloud and MLOps experience.

Responsabilités

• Lead design and deployment of Gen AI and agentic AI solutions.
• Guide architecture decisions and delivery practices.
• Move ideas from experimentation to production monitoring.
• Collaborate with product, engineering, risk and stakeholders.
• Establish governance and responsible AI practices.
• Develop the chapter through coaching and knowledge sharing.

Connaissances

Leadership

Gen AI

Machine Learning

Stakeholder comms

Formation

Tertiary qualification

Outils

Python

SQL

Cloud (AWS/Azure)

MLOps

Description du poste

Chapter Lead Data Science - Fraud, Scams & Cyber

• 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,cloudand 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, softwareengineeringand 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 andscamsdomain. 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,deploymentand ongoing monitoring, while ensuring solutions are safe,scalableand 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 andscams. The team works closely with product, engineering, platform, architecture,MLOpsand risk partners to deliver trusted solutions thatoperatereliably at scale.

We value curiosity, pragmatism,inclusionand continuous improvement. We share knowledge, test emerging methodsthoughtfullyandmaintaina strong focus on customer outcomes, technicalqualityand 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 andscamschallenges.
• Progress solutions from exploration and experimentation through evaluation, validation,deploymentand ongoing monitoring.
• Design and develop Generative AI capabilities, including Retrieval-Augmented Generation, orchestration,guardrailsand agentic AI systems.
• Identifyopportunities to expand the effective and responsible use of data science, machinelearningand AI across fraud andscams.
• Partner with product, engineering, platform, architecture,MLOps, risk and business stakeholders to define problems, evaluate trade-offsand translate technical insights into action.
• Establish strong technical practices across data and feature engineering, model selection, evaluation, testing,documentationand production deployment.
• Lead and develop the chapter through coaching, knowledge sharing, technicalguidanceand 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, developingothersand turning emerging technologies into reliable, practical outcomes.
• Communicate clearly, build trustedrelationshipsand canoperateeffectively across technical,businessand risk contexts.

You will bring:

• Proven experience leading teams or providing significant technical leadership across machine learning, GenerativeAIor agentic AI initiatives.
• A strong foundationin statistics,probabilityand machine learning, with the ability to interpret,evaluateand explain modelbehaviour.
• Hands-on experience designing,developingand evaluatingproductionAI 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,servicesand enterprise platforms, and working withMLOpsteams and production delivery practices.
• Practical knowledge of cloud environments such as AWS or Azure, version control, continuous integration and delivery,containerization,monitoringand observability.
• Experience applying responsible AI, model governance, riskmanagementand 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,Mathematicsor 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.

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