AstraZeneca
Pharmaceutical
AssociateDirector,R&DDataTransformation
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“Associate Director, R&D Data Transformation at AstraZeneca. Skills: Data transformation, Data readiness, Interoperability, Data reuse. Plan and deliver transformation activities. Manage defined workstreams from assessment through implementation”
What You'll Achieve.
Improve readiness; Improve interoperability; Improve reuse; Power AI; Power machine learning; Power advanced analytics; Enable seamless data flow; Increase findability; Unlock value from datasets; Contribute to transformation portfolio; Enable secondary use; Enable cross-functional insight generation; Demonstrate value
Industry & Context.
Analytical skills; Problem-solving skills; Root cause analysis
What They're Looking For.
Must Have
Degree in life sciences, informatics, data science, or related discipline, or equivalent professional experience, Significant experience delivering data transformation, data strategy, or data management initiatives, Demonstrated success designing and executing transformation initiatives with measurable improvements, Knowledge of data management principles, FAIR standards, metadata management, and ontology frameworks, Ability to apply data management principles practically in scientific data environments, Proven ability to influence stakeholders across technical and scientific, Translate complex data concepts into clear recommendations and actionable plans, Experience coaching or mentoring junior colleagues, Analytical and problem-solving skills, Manage competing priorities, Drive outcomes with limited supervision
Nice to Have
Knowledge of pharmaceutical drug discovery and development processes, Familiarity with AI/ML data requirements, Experience enabling data readiness for advanced 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 knowledge graph technologies, Experience applying change management principles or behavioural science approaches to drive adoption
What You'll Do.
Plan and deliver transformation activities
Manage defined workstreams from assessment through implementation
Provide expert guidance on enterprise and industry data
Identify and resolve interoperability challenges
Develop and implement cataloguing approaches
Develop and implement metadata enrichment strategies
Increase findability and unlock value from datasets
Own and deliver defined transformation workstreams
Ensure quality outcomes
Contribute to the broader transformation portfolio
Coach and support junior team members
Share specialist knowledge across the team
Serve as a recognised expert
Contribute to the development and refinement of the
Provide evidence-based analysis and recommendations
Identify transformation opportunities
Present findings and proposals to senior stakeholders
Translate strategic priorities into detailed transformation designs
Translate strategic priorities into delivery plans
Provide specialist advice
Serve as a first point of contact for
Own and deliver end-to-end transformation workstreams
Conduct current-state assessment
Apply the transformation methodology
Contribute to methodology improvement
Conduct assessments of R&D data domains
Evaluate interoperability
Produce actionable recommendations
Partner with data domain owners
Partner with scientists
Partner with R&D functional teams
Address readiness gaps
Address interoperability gaps
Monitor and manage risks
Monitor and manage issues
Monitor and manage dependencies
Contribute to stage-gate reviews
Deliver post-implementation evaluations
Provide expert guidance on FAIR principles
Provide expert guidance on data standards
Provide expert guidance on ontology application
Implement standards in practice
Analyse and resolve interoperability barriers
Design modular solutions
Design reusable solutions
Advise on applicability of industry standards
Develop and implement practices that increase discoverability
Develop and implement practices that increase contextual richness
Enable cross-functional insight generation
Track and report reuse metrics
Use evidence to inform prioritisation
Develop reusable frameworks
Develop reusable templates
Develop reusable guidance materials
Enable scalable adoption of data reuse practices
Provide technical guidance
Provide quality assurance on deliverables
Support professional development
Support workload planning
Support prioritisation
Escalate capacity or capability gaps
How You'll Work.
Team & Collaboration
R&D functions; AI for Science Innovation; Enterprise AI Technology; IT; Data domain owners; R&D functional teams; Technology teams; Data Programmes; Project leadership; Change management; Data automation
Communication Scope
Present findings; Present proposals; Specialist advice
Process & Methodology
Roadmap development, Prioritisation criteria, Delivery playbooks, Workload planning, Prioritisation
Full Job Description
**Introduction to the Role** The Associate Director, R&D Data Transformation is a recognised expert practitioner who plans, directs, and delivers transformation initiatives that improve the readiness, interoperability, and reuse of data across AstraZeneca's R&D data estate. This role brings strong technical expertise, project ownership, and coaching capability to ensure R&D data is AI-ready and "available by default," directly supporting the AI30 ambition and Ambition 2030. The Associate Director leads defined workstreams and acts as a first point of contact for specialist queries — partnering with R&D functions, AI for Science Innovation, Enterprise AI Technology, and IT to design and deliver solutions that enable seamless data flow across the R&D lifecycle. **Scope of Accountability** You will operate as an expert practitioner within the R&D Data Transformation team, with accountability across the following areas: **R &D Data Readiness:** Plan and deliver transformation activities that bring priority R&D data assets to the quality, structure, and completeness standards required to power AI, machine learning, and advanced analytics, managing defined workstreams from assessment through implementation. **Interoperability and Standards:** Provide expert guidance on enterprise and industry data standards (ontologies, vocabularies, schemas, FAIR principles) within assigned transformation workstreams, identifying and resolving interoperability challenges across R&D systems and platforms. **Data Reuse and Discoverability:** Develop and implement cataloguing approaches and metadata enrichment strategies within assigned domains that increase findability and unlock value from historical and emerging R&D datasets. **Transformation Delivery:** Own and deliver defined transformation workstreams — applying established methodology, managing dependencies, and ensuring quality outcomes that contribute to the broader transformation portfolio. **Coaching and Expertise:** Coach and su
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