Chapter Lead (Executive Manager), AI & General Model Validation

About the role

CommBank's AI & General Model Validation (AIGMV) team provides independent technical challenge and review of the Group's most important machine learning and AI models across business and support units. As Chapter Lead (Executive Manager) in the Data Science and Gen AI, Model Risk Validation team, you will lead a group of data scientists across Australia and India, helping ensure AI models are safe, scalable and aligned with customer and regulatory expectations. The role sits within Risk Management and involves occasional international travel.

Key responsibilities

• Lead a chapter of data scientists across Australia and India, including hiring, coaching, performance and career development
• Plan and run the chapter's validation work, from prioritising engagements to delivering on time at a consistent quality bar
• Lead the development of validation tooling and methods, challenging methodology and evidence
• Represent the chapter with model developers, senior business leaders, product owners and governance experts
• Engage senior stakeholders, including General Managers, model owners, central AI teams and internal audit

Required profile

• Proven experience leading high-performing multidisciplinary teams with Gen AI, data science and software engineering skill sets, and mentoring data scientists
• Project management experience in agile environments and interest in automation tools
• Strong stakeholder management and communication skills, with a growth mindset
• Degree in Statistics, Data Science, Computer Science, Actuarial Science, Engineering or a related field, or equivalent industry experience

Required skills

• Deep working knowledge of GenAI and agentic architectures, with hands-on experience designing, building and evaluating AI solutions; fine-tuning transformer models is a plus
• Advanced Python, with strong GitHub, version control and CI/CD experience; SQL, R or TypeScript is a plus
• Ability to read unfamiliar codebases, reproduce evaluation pipelines, write test harnesses and inspect trace data
• Practical experience with AI coding agents (e.g. Claude Code, Codex, GitHub Copilot)
• Working knowledge of traditional machine learning models such as random forests and XGBoost
• Experience with GenAI frameworks such as LangChain, LangGraph, OpenAI Agents SDK, Pydantic, Langfuse/HoneyHive, and PyTorch or AWS SageMaker