AstraZeneca
Pharmaceutical
SeniorSpecialist,R&DDataTransformation
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“Senior Specialist, R&D Data Transformation at AstraZeneca. Skills: Data Transformation, Data Management, AI/ML Data Readiness, Interoperability. Coordinate and deliver transformation activities. Execute transformation activities”
Industry & Context.
Analytical rigour; Sound judgement; Navigate ambiguity; Problem-solving; Data analysis
What They're Looking For.
Must Have
Degree in life sciences, informatics, data science, or related discipline, or equivalent professional experience, Experience delivering data transformation, data management, or data quality initiatives, Experience within pharmaceutical R&D or a regulated scientific environment, Demonstrated ability to execute transformation or improvement activities with measurable outcomes, Good knowledge of data management principles, FAIR standards, and metadata management, Analytical and problem-solving skills, Effective communication skills, Ability to work both independently and collaboratively
Nice to Have
Knowledge of pharmaceutical drug discovery and development processes, Familiarity with AI/ML data requirements, Experience supporting data readiness for analytics and machine learning use cases, Experience with enterprise data platforms or cloud-based data ecosystems, Experience with data cataloguing tools, metadata management platforms, or data quality tooling, Exposure to change management principles, Experience supporting the adoption of new data practices within scientific teams
What You'll Do.
Coordinate and deliver transformation activities
Execute transformation activities
Support implementation of enterprise and industry data standards
Develop and maintain cataloguing processes
Develop and maintain metadata enrichment activities
Develop and maintain reporting for data reuse
Coordinate and deliver transformation activities
Apply established transformation methodology
Conduct current-state assessments of R&D data assets
Work directly with data domain owners and R&D
Support implementation of solutions
Track and report progress
Prepare materials and evidence of delivery
Support implementation of FAIR principles
Support implementation of data standards
Support implementation of ontologies
Identify and document interoperability barriers
Propose solutions for interoperability barriers
Escalate complex issues
Maintain awareness of relevant industry standards
Support practical application of standards
Execute cataloguing activities
Execute metadata enrichment activities
and report reuse metrics
Contribute to development of reusable frameworks
Contribute to development of templates
Contribute to development of guidance materials
Develop and maintain tools
Develop and maintain templates
Develop and maintain processes
Identify areas for process improvement
Propose and implement enhancements
Ensure business continuity of transformation processes
Maintain documentation and knowledge repositories
Solve complex problems
Analyse R&D data landscapes
Identify improvement opportunities
Synthesise findings into clear summaries
Present recommendations
Support preparation of business cases
Support preparation of progress reports
Support preparation of stakeholder communications
Partner with Data Programmes
Coordinate transformation activity milestones
Collaborate with peers within the R&D Data Transformation
Maintain methodological consistency
Support coherent delivery across the portfolio
Build effective working relationships with R&D functional teams
Build effective working relationships with data domain contacts
Maintain regular communication
Support co-design activities
Contribute to Enterprise Data governance processes
Ensure transformation artefacts and documentation are transparent
Ensure transformation artefacts and documentation are complete
Ensure transformation artefacts and documentation are aligned to
How You'll Work.
Team & Collaboration
Partner with Data Programmes; Collaborate with peers; Build working relationships; Enterprise Data governance
Communication Scope
Present findings; Build relationships; Stakeholder communications
Process & Methodology
Stage-gate reviews, Methodology, Prioritisation criteria, Delivery playbooks
Full Job Description
**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
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