Scope of Accountability

• R&D Data Readiness: Define and execute transformation plans that bring R&D data assets to the quality, structure and completeness standards required to power AI, machine learning and advanced analytics at every stage of the R&D lifecycle.
• Interoperability and Standards: Champion alignment to enterprise and industry data standards (ontologies, vocabularies, schemas, FAIR principles) within transformation initiatives, partnering with the R&D Semantic Layer lead who drives standards adoption across the R&D data estate.
• Data Reuse and Discoverability: Establish practices, cataloguing and metadata enrichment that maximise findability and reuse of R&D data assets, reducing duplication and unlocking latent value from historical and emerging datasets.
• Transformation Delivery: Lead a portfolio of transformation initiatives — from assessment and prioritisation through design, execution and benefits realisation — in partnershi...