AI Transformation Lead - Supply Chain

About the role

The AI Transformation Lead - Supply Chain drives the adoption and scaling of artificial intelligence across Resmed's Supply Chain team. The role identifies high-value opportunities, turns them into practical AI-enabled solutions and partners with cross-functional teams to deliver measurable outcomes. It acts as a bridge between supply chain, digital, data and technology teams, and builds a community of practice, governance frameworks and sustainable adoption of AI.

Key responsibilities

• Define a supply chain AI transformation roadmap by identifying and prioritizing high-value use cases aligned with business objectives.
• Own and maintain an AI initiative portfolio, covering ideation through deployment, scaling, monitoring and optimization.
• Lead AI pilots and proofs of concept with data scientists, engineers and business stakeholders.
• Deploy AI, machine learning, generative AI and automation solutions that deliver measurable outcomes.
• Partner with data and analytics teams to improve data quality, accessibility and governance.
• Establish AI governance frameworks, guardrails and responsible AI practices.
• Drive stakeholder engagement, change management and value realization, acting as a trusted advisor on AI opportunities and risks.
• Develop AI playbooks, standards, best practices and operating models, and build an AI community of practice through workshops and training.
• Track and communicate business value, monitor emerging AI technologies and collaborate with enterprise architecture and technology teams on AI platforms and vendors.

Required profile

• Bachelor's degree in supply chain management, engineering, computer science, data science, business, information systems or a related field; advanced degree preferred.
• 5+ years leading AI, digital transformation, advanced analytics, automation or technology-enabled initiatives in supply chain, operations, manufacturing or related fields.
• Demonstrated experience deploying AI, machine learning, generative AI or intelligent automation from concept to production.
• Experience setting up AI governance, standards and controls, and building AI adoption frameworks, communities of practice or training programmes.
• Nice to have: experience in large global organizations, enterprise AI centers of excellence or leading AI/data science teams.