Senior Manager, Data Science – GenAI & Agentic AI

• Lead and develop a high-performing team delivering production-grade GenAI and multi-agent AI solutions
• Provide hands-on technical leadership across architecture, prototyping, evaluation and deployment
• Take end-to-end ownership, proactively creating momentum and delivering measurable customer and business outcomes

As a Senior Manager within Business Bank’s Customer, Channels & Data AI Centre of Excellence, you will lead a team responsible for delivering high-impact AI and Generative AI solutions.

This is a hands-on leadership role for someone who can both lead people and build. You will be accountable for turning ambiguous business opportunities into well-designed solutions that are deployed, adopted and delivering measurable value.

You will stay close to the technical work—shaping solution architecture, prototyping critical components, reviewing designs and code, and helping the team resolve difficult delivery challenges. You will bring practical experience in agentic AI, including the design and implementation of multi-agent systems.

Success in this role requires a strong ownership mindset. You will be expected to identify what needs to happen, create clarity where it does not yet exist, proactively remove roadblocks and follow through until the intended outcome is achieved. This is not a strategy-only or coordination-only role.

Our team enables Business Banking’s ambition to become Australia’s leading commercial bank through the responsible application of AI across customer, channel and data experiences.

We focus on delivering AI solutions that improve the experience of our customers, increase the effectiveness of our bankers and create sustainable business value.

Working across Business Bank and the broader Group, we combine deep business understanding, data science, AI engineering and responsible AI practices to take solutions from early exploration through to production deployment and adoption.

• Lead, coach and develop a high-performing team of Data Scientists and AI specialists, setting clear expectations for technical quality, accountability and delivery.
• Own strategic AI and GenAI initiatives from opportunity identification and problem definition through solution design, build, deployment, adoption and ongoing performance management.
• Provide hands-on technical leadership, including solution architecture, prototyping, design and code reviews, technical problem-solving and delivery assurance.
• Design and deliver agentic AI solutions, including multi-agent systems that use orchestration, tool calling, retrieval, reasoning, state management and human oversight.
• Ensure solutions move beyond proof-of-concept stage and are engineered for production reliability, security, maintainability and scale.
• Establish robust approaches to GenAI evaluation, covering solution quality, groundedness, safety, reliability, latency, cost and business effectiveness.
• Proactively identify delivery risks, capability gaps and unresolved dependencies, then take ownership of driving them to resolution.
• Make effective decisions in ambiguous environments, creating a clear path forward rather than waiting for complete information or detailed direction.
• Partner with Product, Engineering, Architecture, Risk, Cyber, Legal and business stakeholders to deliver end-to-end outcomes.
• Influence senior stakeholders by translating complex technical concepts into clear choices, trade-offs and business implications.
• Define success measures and use data to demonstrate customer impact, operational performance and realised business value.
• Promote strong model governance, responsible AI, privacy and risk-management practices throughout the solution lifecycle.
• Build reusable AI patterns, platforms and delivery practices that increase the speed and quality of future initiatives.
• Foster a culture of curiosity, constructive challenge, continuous learning and shared accountability.

• Are experienced people leaders with a track record of building, developing and retaining high-performing Data Science, Machine Learning or AI teams.
• Have recent, hands-on experience designing and delivering production GenAI applications, particularly agentic AI or multi-agent systems.
• Can demonstrate personal technical contribution to AI delivery—not only program management, vendor management, strategy development or proof-of-concept oversight.
• Have successfully taken complex AI solutions from an ambiguous business problem through to production deployment, adoption and measurable impact.
• Bring a strong sense of ownership and accountability, with examples of proactively identifying problems, creating momentum and delivering outcomes beyond the boundaries of their formal remit.
• Are comfortable moving between executive stakeholder conversations, solution architecture discussions and detailed technical reviews.
• Combine strong Data Science and Machine Learning foundations with practical software engineering and production delivery experience.
• Understand how to balance experimentation and innovation with the reliability, security and governance requirements of a large regulated organisation.
• Have experience working across multidisciplinary teams involving Product, Engineering, Architecture, Design, Risk and business subject-matter experts.
• Can challenge constructively, make sound decisions with incomplete information and maintain momentum through complex organisational dependencies.
• Communicate technical concepts, limitations and trade-offs clearly to both technical and non-technical audiences.
• Are passionate about applying AI responsibly to solve meaningful customer and business problems.

We are looking for a leader who remains technically active and credible. You will not be expected to write every line of code, but you should be capable of diving into the detail, building prototypes, assessing technical quality and guiding the team through difficult implementation decisions.

Experience across several of the following areas will be highly regarded:

• Agentic and multi-agent AI: agent orchestration, role and task decomposition, planning, tool use, hand-offs, routing, shared state, memory, failure recovery and human-in-the-loop controls.
• Generative AI application development: large language models, prompt and context engineering, structured outputs, function calling, retrieval-augmented generation and model selection.
• GenAI evaluation: offline and online evaluation, groundedness, factuality, safety, task completion, reliability, latency, cost and business outcome measurement.
• Production AI engineering: Python, APIs, automated testing, version control, CI/CD, observability and scalable service design.
• Machine Learning and Data Science: model development, experimentation, feature engineering, statistical analysis and performance assessment.
• MLOps and LLMOps: model and prompt versioning, deployment pipelines, monitoring, feedback loops, incident management and continuous improvement.
• Cloud AI platforms: implementation and operation of AI and Machine Learning workloads in enterprise cloud environments.
• Responsible AI and governance: model risk, privacy, security, explainability, auditability and appropriate human oversight.