AsAssociate Director, R&D Data Transformation
On-site · Barcelona, Catalonia, Spain
Job Summary
Lead transformation initiatives to improve readiness, interoperability, and reuse of R&D data across AstraZeneca's R&D data estate. Plan and deliver transformation activities, own defined workstreams, and coach junior team members while partnering with R&D functions, AI for Science Innovation, Enterprise AI Technology, and IT to design and deliver data-flow solutions that enable AI-ready data and seamless data movement across the R&D lifecycle. Apply FAIR data standards, metadata management, and ontology frameworks to advance data interoperability, cataloguing, and reuse; drive end-to-end transformation from assessment through implementation, including risk management and post-implementation sustainment. Coordinate with data domain owners, scientists, and functional teams to translate strategic priorities into actionable designs, ensuring quality outcomes and measurable improvements in data readiness and reuse.
Required Qualifications
- 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
- 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
- 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
- Desirable: Knowledge of pharmaceutical drug discovery and development processes, including data flows across preclinical, clinical, regulatory, and manufacturing domains
- Desirable: Familiarity with AI/ML data requirements and experience enabling data readiness for advanced analytics and machine learning use cases
- Desirable: Experience with enterprise data platforms or cloud-based data ecosystems (e.g., Databricks, Snowflake, AWS/Azure data services)
- Desirable: Experience with data cataloguing tools, metadata management platforms, or knowledge graph technologies
- Desirable: Experience applying change management principles or behavioural science approaches to drive adoption of new data practices and standards
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