Director, R&D Data Transformation

The Director, R&D Data Transformation leads the strategic programme of work that drives measurable improvement in the readiness, interoperability and reuse of data across AstraZeneca's R&D data estate. Reporting to the Head of R&D Data Office within Enterprise Data Enablement, this role defines R&D transformation priorities and partners with Data Programmes to execute initiatives that make R&D data AI-ready and "available by default," directly supporting the AI30 ambition and Ambition 2030. The Director leads their team and partners closely with R&D functions, AI for Science Innovation, Enterprise AI Technology, and IT to ensure data flows seamlessly across the R&D lifecycle.

Scope of accountability:

You will lead R&D Data Transformation as an integrated programme within the R&D Data Office directly reporting to the Head of R&D Data Office, with accountability across the following areas:

• R&D Data Readiness: Define and execute transformation plans that bring R&D data assets to the quality, structure and completeness standards required to power AI, machine learning and advanced analytics at every stage of the R&D lifecycle.
• Interoperability and Standards: Champion alignment to enterprise and industry data standards (ontologies, vocabularies, schemas, FAIR principles) within transformation initiatives, partnering with the R&D Semantic Layer lead who drives standards adoption across the R&D data estate.
• Data Reuse and Discoverability: Establish practices, cataloguing and metadata enrichment that maximise findability and reuse of R&D data assets, reducing duplication and unlocking latent value from historical and emerging datasets.
• Transformation Delivery: Lead a portfolio of transformation initiatives - from assessment and prioritisation through design, execution and benefits realisation - in partnership with platform, technology and change teams.
• Team Leadership: Build and lead a high-performing team, fostering accountability, collaboration and continuous learning aligned to Enterprise AI Unit principles.

Key accountabilities:

Strategic leadership:

• Own the transformation portfolio priorities, sequencing, and benefits case; partner with Data Programmes (EDP) who provide programme leadership, delivery governance, stage gates, and vendor management to execute against the roadmap.
• Develop and present executive-level business cases and progress reporting that articulate value, sequencing, risk and trade-offs to senior stakeholders.
• Translate enterprise data strategy and emerging R&D needs into prioritised, executable transformation plans that balance pace, quality and sustainability.

Transformation design and execution:

• Lead end-to-end delivery of transformation initiatives - from current-state assessment and gap analysis through implementation, adoption and sustainment.
• Define and apply a consistent transformation methodology (maturity models, prioritisation criteria, delivery playbooks) ensuring rigour, repeatability and scalability.
• Partner with data domain owners and R&D functions to identify high-value opportunities and co-design solutions addressing readiness, interoperability and reuse gaps.

Define portfolio priorities, dependencies, and sequencing criteria; partner with Data Programmes to implement delivery governance, stage-gate reviews, and post-implementation evaluations.

Interoperability and standards adoption:

• Drive alignment to FAIR principles, data standards and ontologies within R&D, working with governance, architecture and domain-expert communities.
• Identify and resolve interoperability barriers between R&D systems, platforms and data stores; champion modular, reusable data infrastructure.
• Partner with AI for Science Innovation to ensure transformation priorities reflect AI/ML data requirements across the R&D lifecycle.

Data reuse and value maximisation:

• Establish practices and governance that increase discoverability and contextual richness of R&D data assets, enabling secondary use and cross-functional insight.
• Define and track reuse metrics (e.g., asset utilisation, time-to-access, duplication reduction) to quantify value and inform prioritisation.
• Champion a culture of "data as an enterprise asset," partnering with the Change Management pillar to embed behaviours supporting data sharing and responsible reuse.

Team leadership and development:

• Recruit, develop and retain a diverse team; set clear objectives and development plans aligned to individual growth and enterprise outcomes.
• Manage workload allocation, capacity planning and performance to deliver against the transformation portfolio within agreed timelines and resources.

Partnerships and governance:

• Partner with Data Programmes (project leadership, change management, data automation) to align transformation milestones with delivery stage gates and change plans.
• Contribute to Enterprise Data governance forums; ensure transformation artefacts and decisions are transparent, auditable and aligned to enterprise standards.
• Build relationships with R&D functional leaders and AI for Science Innovation to maintain alignment and secure sponsorship for transformation priorities.

Essential skills and experience:

• Degree in life sciences, informatics, data science or a related discipline, or equivalent professional experience.
• Extensive experience leading data transformation or data strategy programmes within complex, global organisations; ideally within pharmaceutical R&D or a highly regulated scientific environment.
• Demonstrated success delivering large-scale, multi-year transformation portfolios with measurable improvements in data quality, interoperability or reuse.
• Strong knowledge of data management principles, FAIR standards, metadata management and ontology frameworks.
• Proven ability to influence at senior levels across technical and scientific functions; skilled in translating complex data concepts into compelling business narratives.
• Experience leading and developing diverse teams across multiple seniority levels, with a track record of building high-performing, collaborative cultures.

Desirable:

• Knowledge of pharmaceutical drug discovery and development processes, including data flows across preclinical, clinical, regulatory and manufacturing domains.
• Familiarity with AI/ML data requirements and experience enabling data readiness for advanced analytics and machine learning use cases.
• Experience with enterprise data platforms or cloud-based data ecosystems (e.g., Databricks, Snowflake, AWS/Azure data services).

Experience applying change management principles or behavioural science approaches to drive adoption of new data practices and standards.

#EAI

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.