1 day ago

Senior Manager, Data Science – GenAI & Agentic AI

Commonwealth Bank

Melbourne

1 day ago

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Lead and develop a high-performing team delivering production-grade GenAI and multi-agent AI solutions in the Business Bank's Customer, Channels & Data AI Centre of Excellence. This hands-on leadership role requires technical expertise in agentic AI, strong ownership mindset, and the ability to turn ambiguous business opportunities into measurable outcomes.

Core AIOn-siteFull-timeDirectorAgentic AIMulti-agent Systems

Senior Manager, Data Science – GenAI & Agentic AI

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Core AIOn-siteFull-timeDirectorAgentic AI

Salary

Not specified

Work Location

Melbourne, Victoria, Australia, AU

Work Model

On-site

Employment Type

Full-time

Experience Level

Senior Manager

Core Qualifications

Technical (Must-have)

Agentic AIMulti-agent systemsGenerative AILarge Language ModelsPrompt EngineeringRetrieval-Augmented GenerationPythonAPIsAutomated TestingVersion ControlCI/CDObservabilityMachine LearningData ScienceMLOpsLLMOpsCloud AI PlatformsResponsible AIModel Governance

Soft Skills

LeadershipCoachingMentoringCommunicationStakeholder ManagementProblem SolvingOwnershipAccountabilityCuriosityContinuous LearningConstructive Challenge

Key Responsibilities

•Lead, coach and develop a high-performing team of Data Scientists and AI specialists
•Own strategic AI and GenAI initiatives from opportunity identification 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

Data ScienceGenerative AIAgentic AIMachine LearningFinancial ServicesLeadershipMelbourneFull-timeOn-site

Core Qualifications

Technical (Must-have)

Soft Skills

LeadershipCoachingMentoringCommunicationStakeholder ManagementProblem SolvingOwnershipAccountabilityCuriosityContinuous LearningConstructive Challenge

Key Responsibilities

•Lead, coach and develop a high-performing team of Data Scientists and AI specialists

•Own strategic AI and GenAI initiatives from opportunity identification 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