Are you ready to turn trusted data and agentic AI into measurable impact for patients and the business? This role leads the strategy and delivery of AI-ready data products, AI tools, agents, and orchestration across operations and supply chain, enabling faster, safer, and more insightful decisions. Based in Cork (Alexion Office) or Dublin (College Park), you will guide how data and AI assets are designed, governed, and reused to power automation and decision-making at scale. You will operate at the intersection of business, technology, and compliance—owning product direction, embedding responsible AI, and making sure our solutions are trusted, secure, explainable, and fit-for-purpose. Can you set a bold vision and convert it into pragmatic roadmaps teams can deliver against? Do you thrive in complexity and know how to simplify it for action? Your work will translate priorities into reusable data assets, governed AI products, agentic workflows, and outcomes that matter for colleagues and, ultimately, for patients.
Accountabilities: Strategy and Vision: Define and evolve the strategy for AI-ready data products and AI assets in priority domains, grounded in business value and customer needs, and translate these into product designs and iterative delivery plans.
Governance and Responsible AI: Operationalize governance standards for AI products, tools, agents, and orchestration, aligned to business value, risk appetite, regulatory expectations, and enterprise AI principles.
Roadmaps and Delivery: Create and maintain product roadmaps; lead delivery from inception to maturity with clear milestones, KPIs, and value realization. Backlog Ownership: Own and prioritize feature backlogs; partner with architects, analysts, and engineers to elaborate requirements and drive timely reversal of technical debt.
Lifecycle Governance: Participate in enterprise governance from idea intake and prioritization through design, build, validation, deployment, monitoring, change control, and retirement.
AI Readiness: Lead all aspects of AI readiness assessments for data products and business processes, including data quality, metadata completeness, lineage, access controls, model suitability, timely quality, human-in-the-loop requirements, and operational adoption readiness.
Quality and Assurance: Define quality and assurance criteria for AI-enabled solutions—performance, accuracy, reliability, explainability, bias and fairness, privacy, security, auditability—and ensure ongoing monitoring for drift or degradation. FAIR by Design: Champion agile, iterative development while ensuring data products are Findable, Accessible, Interoperable, and Reusable (FAIR). Release and Dependency Management: Handle cross-release dependencies to land phases and landmarks predictably. Collaborator Influence: Communicate clearly with leaders whose operations are impacted; align on value, readiness, and change plans.
Voice of the Business: Serve as the business voice to focus teams on what matters most and enable enterprise objectives. Documentation and Controls: Own and maintain business metadata, use-case documentation, decision records, risk assessments, control evidence, and quality metrics for specified data and AI products.
Agentic AI Operations: Partner with technical teams to ensure AI agents and orchestration workflows have clear ownership, guardrails, escalation paths, monitoring, and measurable value outcomes before production use. Data Stewardship: Perform Data Stewardship activities where assigned to ensure integrity, access, and reusability.
Impact Progression: Deliver immediate value through prioritized features and readiness improvements; scale impact by maturing governance, accelerating reuse, and enabling trusted automation across multiple functions.
Essential Skills/Experience: -
Experience governing data products and/or AI-enabled products across the lifecycle, with the ability to translate business priorities into practical standards, controls, and adoption pathways.
- Thrives in a fast-paced work environment, comfortable with complexity and uncertainty at times, dedication to deliver outcomes and motivated by the opportunity to rethink our approach to healthcare.
- Ability to handle a cross-functional product development and deployment process, including teams of people with diverse skills to perform the needed tasks
- Comfortable operating in a heavily matrixed organizations with excellent communication skills and intuition to handle the needs of various stakeholders. - Solid Project and / or Product management skills, including handling budgets, timelines and risks.
- Experience bringing innovation to bear on solving complex and multi-dimensional issues from an operational, technical, financial and human perspective. - Understanding of AI product governance principles, including responsible AI, AI risk management, model and prompt evaluation, human oversight, explainability, monitoring, and lifecycle documentation.
- Deep familiarity with data engineering concepts such as ETL, data modelling, data lakes / databases, reference data, master data, etc.
- Experience leading or working on agile development teams - Excellent leadership, communication, influencing, and collaborator management skills
- Experience establishing, communicating, and delivering product roadmaps - Strong analytical and problem-solving abilities with the ability to make data-driven decisions - Ability to rapidly grasp concepts and to handle complexity by simplifying it for others and providing clear directions to delivery teams.
- Familiarity with AI readiness requirements for high-quality AI outcomes, including trusted data foundations, metadata, lineage, access controls, privacy, security, data quality, and reuse standards.
- Ability to collaborate with data scientists, AI engineers, product teams, architects, compliance partners, and business collaborators to define requirements and quality expectations for AI tools, agents, and orchestration workflows
- Ability to handle and influence strategically and persuade tactfully, to obtain desired outcomes while maintaining effective, positive, organizational relationships.
Desirable Skills/Experience:
- Master’s or PhD in a scientific or technical rigor. - 5+ years of relevant experience in pharmaceutical industry with strong knowledge of Drug Development Processes and the diverse sources of information that support, inform and assist in healthcare/clinical research.
- Familiarity with the latest research and thinking and understanding of dynamics affecting the pharmaceutical industry and needs for transformation
. - Familiarity with data science, data visualization, and reporting concepts - Ability to work in fast-paced, dynamic environment and handle multiple streams of work simultaneously
- Experience with generative AI, AI agents, agentic orchestration, MLOps/LLMOps, AI assurance, model governance, or responsible AI frameworks would be advantageous.
Why AstraZeneca: Here, meaningful science meets entrepreneurial energy. You will feel the pace and autonomy of a leading biotech environment backed by the scale, investment, and rigor of a global biopharma. We bring unexpected teams into the same room—engineers, product leaders, clinicians, and compliance experts—to unleash bold thinking rooted in patient need. Our culture values kindness alongside ambition, so you can take ownership, learn fast, and stretch your craft while staying grounded in integrity and responsibility. Your contribution will help transform how data and AI guide critical decisions for people living with devastating and often overlooked conditions, turning everyday work into progress that truly counts.
Date Posted
11-Aug-2026
Closing Date
07-Sep-2026
Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.
