Senior Director, Artificial Intelligence

See Yourself at Telix

The Senior Director, Artificial Intelligence is accountable for establishing, scaling, and operating the enterprise AI capability as a core business function across the organisation. Reporting to the CIO, this role serves as the enterprise authority for AI strategy, operating model, governance, and value delivery. This leader bridges strategy, technology, regulatory compliance, and execution to ensure AI initiatives deliver measurable outcomes across R&D, Clinical, Manufacturing, Quality, Commercial, and Corporate functions. This role ensures that AI is deployed in a safe, compliant, and scalable manner within a highly regulated environment, embedding AI into core business processes without introducing regulatory, ethical, or operational risk.

The role owns the end-to-end enterprise AI lifecycle, including strategy, portfolio prioritisation, operating model design, platform enablement, governance frameworks, and organisational adoption.

Key Accountabilities:

Enterprise Strategy & Value Delivery

• Define and maintain the enterprise AI strategy aligned with corporate objectives and long-term business priorities.
• Translate business challenges into a prioritized AI portfolio with clear value hypotheses, success metrics, and ROI tracking.
• Serve as the enterprise leader accountable for AI value realization, ensuring initiatives deliver measurable business impact.

Portfolio Governance & Decision Authority

• Chair the Enterprise AI Governance Council and own portfolio decision rights, including use-case prioritization, investment allocation, and progression from experimentation to production.
• Establish governance mechanisms to ensure disciplined investment and prevent non-value-generating AI initiatives.
• Act as the escalation point for enterprise-level AI decisions, balancing risk, value, and feasibility.

Operating Model & Delivery Accountability

• Own and continuously evolve the enterprise AI operating model, enabling federated delivery across business units while enforcing mandatory standards, tools, and processes.
• Ensure consistent, repeatable delivery patterns from proof-of-concept through validation, production deployment, monitoring, and lifecycle management (MLOps/LLMOps).
• Hold accountability for successful execution of AI initiatives across federated teams, ensuring adherence to enterprise standards.

Governance, Risk, and Compliance

• Design and operate AI governance frameworks covering model risk, data governance, ethics, privacy, and regulatory compliance.
• Ensure AI solutions comply with applicable regulatory frameworks (e.g. GxP, ICH GCP, 21 CFR Part 11, GDPR, and similar privacy regulations).
• Establish validation, auditability, and change control processes required for regulated AI deployments in clinical, manufacturing, and quality environments.
• Own enterprise AI risk management processes in partnership with Quality, Legal, Compliance, and Privacy functions

Platform & Technology Enablement

• Own the enterprise AI platform strategy and roadmap, including infrastructure, tooling, and data enablement layers.
• Lead build vs. buy decisions and manage strategic vendor and partner ecosystem supporting AI capabilities.
• Ensure scalability, security, and standardization of AI technologies across the enterprise.

Organizational Capability & Adoption

• Lead the Enterprise AI Center of Excellence (CoE), including strategy, architecture, governance, platform enablement, and adoption support.
• Build organizational capability through training, playbooks, and communities of practice to drive adoption at scale.
• Partner with senior leaders across functions to embed AI into business processes and decision-making.

Stakeholder Engagement & Leadership

• Serve as the primary enterprise advisor to senior leadership on AI strategy, risks, and opportunities.
• Influence cross-functional leaders to align on priorities, standards, and enterprise-wide adoption.
• Represent the organization’s AI capability in external forums, partnerships, and industry engagements as appropriate.

Education and Experience:

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field required. Advanced degree (MBA, MS, or PhD) preferred.
• 12+ years of experience in data, analytics, or AI/ML roles, with at least 5+ years in leadership positions
• Proven experience building and scaling enterprise-level AI or data capabilities in complex organizations
• Demonstrated experience operating in regulated environments (e.g., life sciences, pharmaceuticals, healthcare, or similar)
• Experience designing and implementing governance frameworks for AI, data, and model risk management
• Track record of delivering large-scale, cross-functional transformation initiatives with measurable business impact
• Experience managing enterprise vendor ecosystems and technology investments

Key Capabilities:

• Inclusive mindset: Demonstrate an understanding and appreciation for diversity, and actively work to create an inclusive environment where everyone feels valued and respected
• Creativity and innovation: Possess a willingness to think outside the box and come up with unique and creative solutions to challenges
• Commitment to excellence: Take pride in your work and consistently strive for excellence in everything you do
• Results-oriented: Driven to achieve goals and objectives, with a strong focus on delivering measurable results
• Ethical behavior: Act with integrity and demonstrate a commitment to ethical behavior in all interactions with colleagues and stakeholders
• Adaptability: Comfortable working in a dynamic environment, able to adjust to changing priorities, and willing to take on new challenges
• Strong communication skills: Able to communicate effectively with colleagues and stakeholders at all levels, using clear and concise language
• Collaboration: Work effectively as part of a team, actively sharing knowledge and expertise to achieve common goals
• Resilience: Demonstrate the ability to bounce back from setbacks and persevere in the face of challenges
• Continuous learning: Show a commitment to ongoing learning and professional development, continually seeking out opportunities to expand your knowledge and skills