Introduction to the Role
The Manager, R&D Data Transformation coordinates and delivers transformation activities that improve the readiness, interoperability, and reuse of data across AstraZeneca's R&D data estate. This role brings strong analytical capability, delivery rigour, and growing domain expertise to execute transformation initiatives that ensure R&D data is AI-ready and "available by default," directly supporting the AI30 ambition and Ambition 2030. The Manager delivers within defined workstreams — partnering with R&D functions, AI for Science Innovation, Enterprise AI Technology, and IT to implement solutions that enable seamless data flow across the R&D lifecycle.
Scope of Accountability
You will operate as a practitioner within the R&D Data Transformation team, with accountability across the following areas:
R&D Data Readiness: Execute transformation activities that bring R&D data assets to the quality, structure, and completeness standards required to power AI, machine learning, and advanced analytics, delivering defined tasks and workstream components under the guidance of senior team members.
Interoperability and Standards: Support the implementation of enterprise and industry data standards (ontologies, vocabularies, schemas, FAIR principles) within assigned transformation activities, working with domain teams to resolve interoperability issues in practice.
Data Reuse and Discoverability: Develop and maintain cataloguing processes, metadata enrichment activities, and reporting that support findability and reuse of R&D data assets within assigned domains.
Transformation Delivery: Coordinate and deliver defined transformation activities — applying established methodology and tools, managing task-level dependencies, and ensuring quality and completeness of outputs.
Process and Tool Development: Develop and improve existing tools, templates, and processes used by the R&D Data Transformation team to identify improvement areas, track progress, and ensure business continuity of the function.
Key Accountabilities
Transformation Execution and Coordination
- Coordinate and deliver assigned transformation activities within defined workstreams — from data assessment and gap analysis through implementation, documentation, and handover — ensuring outputs meet quality standards and timelines.
- Apply the established transformation methodology (maturity models, prioritisation criteria, delivery playbooks) within assigned activities, flagging opportunities for improvement based on practical experience.
-Conduct current-state assessments of R&D data assets within assigned domains, documenting readiness, interoperability, and reuse maturity against defined criteria and producing clear findings for review.
- Work directly with data domain owners and R&D functional teams to gather requirements, validate findings, and support the implementation of solutions addressing readiness, interoperability, and reuse gaps.
- Track and report progress, risks, and issues within assigned activities; support stage-gate reviews by preparing materials and evidence of delivery against milestones.
Interoperability and Standards Implementation
- Support the implementation of FAIR principles, data standards, and ontologies within assigned R&D domains, working hands-on with domain teams to apply standards in practice.
- Identify and document interoperability barriers between R&D systems, platforms, and data stores within assigned scope; propose solutions and escalate complex issues to senior team members.
- Maintain awareness of relevant industry standards (e.g., CDISC, OMOP, biomedical ontologies) and support their practical application within transformation activities.
Data Reuse and Discoverability
- Execute cataloguing and metadata enrichment activities that increase discoverability and contextual richness of R&D data assets within assigned domains.
- Collect, maintain, and report reuse metrics (e.g., asset utilisation, time-to-access, duplication reduction) for assigned domains, supporting evidence-based prioritisation and value demonstration.
- Contribute to the development of reusable frameworks, templates, and guidance materials that support scalable adoption of data reuse practices.
Process and Tool Development
- Develop and maintain tools, templates, and processes used by the R&D Data Transformation team to conduct assessments, track transformation progress, and report outcomes.
- Identify areas for process improvement within existing transformation workflows; propose and implement enhancements that increase efficiency, consistency, or quality.
- Ensure business continuity of transformation processes and reporting within assigned scope, maintaining documentation and knowledge repositories to support team resilience.
Problem-Solving and Analysis
- Solve complex problems within a variety of data transformation scenarios, applying analytical rigour and sound judgement to navigate ambiguity and deliver practical outcomes.
- Analyse R&D data landscapes within assigned scope to identify improvement opportunities, synthesise findings into clear summaries, and present recommendations to senior team members.
- Support the preparation of business cases, progress reports, and stakeholder communications by providing accurate data, analysis, and evidence from assigned activities.
Partnerships and Collaboration
- Partner with Data Programmes (project leadership, change management, data automation) to coordinate transformation activity milestones with delivery timelines and change plans.
- Collaborate with peers within the R&D Data Transformation team to share knowledge, maintain methodological consistency, and support coherent delivery across the portfolio.
- Build effective working relationships with R&D functional teams and data domain contacts within assigned scope, maintaining regular communication and supporting co-design activities.
- Contribute to Enterprise Data governance processes by ensuring transformation artefacts and documentation within assigned activities are transparent, complete, and aligned to enterprise standards.
Essential Skills and Experience
- Degree in life sciences, informatics, data science, or a related discipline, or equivalent professional experience.
- Experience delivering data transformation, data management, or data quality initiatives within complex organisations; experience within pharmaceutical R&D or a regulated scientific environment is advantageous.
- Demonstrated ability to execute transformation or improvement activities with measurable outcomes, working within established methodologies and frameworks.
- Good knowledge of data management principles, FAIR standards, and metadata management, with the ability to apply these in practical data improvement scenarios.
- Strong analytical and problem-solving skills with the ability to navigate complex data environments, manage multiple tasks, and deliver quality outputs within agreed timelines.
- Effective communication skills with the ability to present findings clearly to both technical and non-technical audiences and build productive working relationships across functions.
- Ability to work both independently and collaboratively within a team, demonstrating initiative, attention to detail, and a commitment to continuous improvement.
Desirable
- Knowledge of pharmaceutical drug discovery and development processes, including data flows across preclinical, clinical, or regulatory domains.
- Familiarity with AI/ML data requirements or experience supporting data readiness for 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 with data cataloguing tools, metadata management platforms, or data quality tooling.
- Exposure to change management principles or experience supporting the adoption of new data practices within scientific teams.
#EAI
Date Posted
10-jun-2026Closing Date
21-jun-2026AstraZeneca 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.