Associate Director, R&D Data Transformation

Astrazeneca Plc; Laza; Presencial; EUR 90.000 - 140.000; Jornada completa

Descripción de la vacante

AstraZeneca PLC is seeking an Associate Director, R&D Data Transformation to lead transformation initiatives that improve data readiness, interoperability, and reuse across the R&D data estate. You will own end-to-end workstreams, coach junior team members, and collaborate with R&D functions to enable AI-ready data for science innovation.

Lead design and delivery of data readiness, interoperability, and reuse programs, applying FAIR principles and industry standards to advance

Formación

• Degree in life sciences, informatics, data science, or a related discipline, or equivalent professional experience.
• Significant experience delivering data transformation, data strategy, or data management initiatives within complex, global organisations; ideally within pharmaceutical R&D or a highly regulated scientific environment.
• Demonstrated success designing and executing transformation initiatives with measurable improvements in data quality, interoperability, or reuse.
• Strong knowledge of data management principles, FAIR standards, metadata management, and ontology frameworks, with the ability to apply these practically in scientific data environments.
• Proven ability to influence stakeholders across technical and scientific functions; skilled in translating complex data concepts into clear recommendations and actionable plans.
• Experience coaching or mentoring junior colleagues, with a track record of supporting capability development and maintaining quality standards across team deliverables.
• Strong analytical and problem-solving skills with the ability to manage competing priorities and drive outcomes with limited supervision.
• 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 with data cataloguing tools, metadata management platforms, or knowledge graph technologies.
• Experience applying change management principles or behavioural science approaches to drive adoption of new data practices and standards.

Responsabilidades

• Own and deliver end-to-end transformation workstreams — from current-state assessment and gap analysis through solution design, implementation, adoption, and sustainment.
• Apply the transformation methodology (maturity models, prioritisation criteria, delivery playbooks) within assigned workstreams, contributing to its continuous improvement based on practical experience and lessons learned.
• Conduct assessments of R&D data domains to evaluate readiness, interoperability, and reuse maturity; produce actionable recommendations with clear sequencing and dependencies.
• Partner with data domain owners, scientists, and R&D functional teams to co-design solutions that address specific readiness, interoperability, and reuse gaps within assigned scope.
• Monitor and manage risks, issues, and dependencies within assigned workstreams; contribute to stage-gate reviews and deliver post-implementation evaluations.
• Coach and mentor junior team members (Manager level), providing technical guidance, quality assurance on deliverables, and support for professional development.
• Share knowledge and specialist expertise across the R&D Data Transformation team, contributing to methodological consistency and collective capability development.

Conocimientos

Data Transformation

Data Strategy

Data Management

FAIR Standards

Metadata Management

Ontology Frameworks

Stakeholder Influence

Coaching

Analytical Skills

Educación

Degree in life sciences, informatics, data science, or related

Herramientas

Databricks

Snowflake

AWS/Azure data services

Data cataloguing tools

Metadata management platforms

Knowledge graph

Descripción del empleo

Associate Director, R&D Data Transformation

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 support junior team members, share specialist knowledge across the team, and serve as a recognised expert and first point of contact for data transformation queries within the R&D Data Office.

Key Accountabilities

Strategic Contribution and Expert Guidance

Contribute to the development and refinement of the R&D Data Transformation roadmap, providing evidence-based analysis and recommendations informed by domain expertise and cross-functional insight.

Identify transformation opportunities through analysis of R&D data domains, emerging AI/ML requirements, and stakeholder pain points; present findings and proposals to senior stakeholders with clarity and credibility.

Translate strategic priorities into detailed transformation designs and delivery plans for assigned workstreams, balancing pace, quality, and sustainability.

Act as a recognised expert within the team and broader R&D Data Office, providing specialist advice and serving as a first point of contact for complex data transformation queries.

Transformation Design and Execution

Own and deliver end-to-end transformation workstreams — from current-state assessment and gap analysis through solution design, implementation, adoption, and sustainment.

Apply the transformation methodology (maturity models, prioritisation criteria, delivery playbooks) within assigned workstreams, contributing to its continuous improvement based on practical experience and lessons learned.

Conduct assessments of R&D data domains to evaluate readiness, interoperability, and reuse maturity; produce actionable recommendations with clear sequencing and dependencies.

Partner with data domain owners, scientists, and R&D functional teams to co-design solutions that address specific readiness, interoperability, and reuse gaps within assigned scope.

Monitor and manage risks, issues, and dependencies within assigned workstreams; contribute to stage-gate reviews and deliver post-implementation evaluations.

Interoperability and Standards Adoption

Provide expert guidance on FAIR principles, data standards, and ontology application within R&D contexts; work directly with domain teams to implement standards in practice within assigned workstreams.

Analyse and resolve interoperability barriers between R&D systems, platforms, and data stores within assigned domains; design modular, reusable solutions that can scale beyond individual workstreams.

Stay current with industry standards evolution (e.g., CDISC, OMOP, biomedical ontologies) and advise on their applicability within the AstraZeneca R&D context.

Data Reuse and Value Maximisation

Develop and implement practices that increase discoverability and contextual richness of R&D data assets within assigned domains, enabling secondary use and cross-functional insight generation.

Track and report reuse metrics (e.g., asset utilisation, time-to-access, duplication reduction) for assigned domains; use evidence to inform prioritisation and demonstrate value.

Develop reusable frameworks, templates, and guidance materials that enable scalable adoption of data reuse practices across the portfolio.

Coaching and Team Development

Coach and mentor junior team members (Manager level), providing technical guidance, quality assurance on deliverables, and support for professional development.

Share knowledge and specialist expertise across the R&D Data Transformation team, contributing to methodological consistency and collective capability development.

Support workload planning and prioritisation within assigned workstreams, escalating capacity or capability gaps to the Team Lead as needed.

Partnerships and Collaboration

Partner with Data Programmes (project leadership, change management, data automation) to align workstream milestones with delivery stage gates and change plans.

Build effective working relationships with R&D functional teams, data domain owners, and technology teams to enable co-design and effective delivery within assigned scope.

Contribute to Enterprise Data governance forums; ensure transformation artefacts and decisions within assigned workstreams are transparent, auditable, and aligned to enterprise standards.

Essential Skills and Experience

Degree in life sciences, informatics, data science, or a related discipline, or equivalent professional experience.

Significant experience delivering data transformation, data strategy, or data management initiatives within complex, global organisations; ideally within pharmaceutical R&D or a highly regulated scientific environment.

Demonstrated success designing and executing transformation initiatives with measurable improvements in data quality, interoperability, or reuse.

Strong knowledge of data management principles, FAIR standards, metadata management, and ontology frameworks, with the ability to apply these practically in scientific data environments.

Proven ability to influence stakeholders across technical and scientific functions; skilled in translating complex data concepts into clear recommendations and actionable plans.

Experience coaching or mentoring junior colleagues, with a track record of supporting capability development and maintaining quality standards across team deliverables.

Strong analytical and problem-solving skills with the ability to manage competing priorities and drive outcomes with limited supervision.

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 with data cataloguing tools, metadata management platforms, or knowledge graph technologies.

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

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 with data cataloguing tools, metadata management platforms, or knowledge graph technologies.

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

#EAI

Date Posted

11-jun-2026

Closing Date

21-jun-2026

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.