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Westpac

Banking and financial services for customers

AI Science Lead - Everyday Banking & Consumer Finance

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • At least 8 years of experience in data science and applied AI, including senior leadership, with recent hands-on practice in banking, financial services, or another complex, highly regulated environment with comparable control expectations.
  • A track record of taking predictive, optimisation, generative AI, or agentic solutions beyond proof of concept into controlled production use.
  • Deep practical expertise in predictive modelling, optimisation, and experimentation, with working knowledge of generative AI and agentic workflows.
  • Sound understanding of model validation, fairness, explainability, human oversight, lifecycle governance, and operational resilience, including MLOps and LLMOps.
  • Proven people leadership, including building capability, coaching technical talent, setting direction, performance management, and succession planning.
  • Commercial judgement and the ability to connect AI delivery to customer outcomes and business value.
  • Credibility with senior business, Product, Technology, and Risk stakeholders, with the ability to explain complex trade-offs clearly and offer constructive technical review.
  • Exposure to everyday banking or consumer finance decision science, such as pricing, credit decisioning, acquisition, retention, or engagement.
  • Experience contributing reusable models, agent patterns, or evaluation approaches across a wider practice or community.

Responsibilities

  • Own the Everyday Banking and Consumer Finance AI strategy and roadmap with business leaders and Product Owners, aligned to divisional and enterprise AI strategies and approved capability direction.
  • Frame and prioritise use cases with clear problems, benefits, success measures, data needs, ownership, and risk classification.
  • Lead technical direction across predictive modelling, optimisation, generative AI, and agentic workflows for pricing, credit decisioning, acquisition, retention, engagement, and automation.
  • Ensure end-to-end solution quality across design, experimentation, validation, deployment, monitoring, and retirement, with rigorous technical review before release.
  • Ensure every solution meets Group AI risk policies by embedding privacy, security, model risk, Responsible AI, explainability, fairness, human oversight, and operational resilience.
  • Coordinate AI Science, data engineering, Technology, Product, and Risk teams around agreed interfaces, plans, and accountable owners.
  • Monitor delivery, adoption, model performance, and realised value, intervening or escalating where outcomes, controls, or timelines are at risk.
  • Build a high-performing, inclusive AI Science team through clear goals, coaching, performance management, development planning, and succession for key technical capabilities.
  • Build reusable models, components, and agent patterns across both product lines to reduce duplication and speed up delivery.

About the Company

Westpac

Banking and financial services for customers

Westpac is an Australian bank offering a wide range of financial services for individuals and businesses, including everyday banking, loans, payments, and digital banking.

Company size
10,001+
Company stage
IPO
Headquarters
Sydney NSW, Australia
Founded
1817

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