Skills:

Architecture

Job type:

Full-time

• Design computational data models, reusable schemas, and structured data frameworks that improve interoperability, consistency, and machine usability across the R&D data ecosystem.
• Define reusable entity structures, metadata patterns, relationships, and data contracts that support integration across experimentation systems, analytics environments, and digital products.
• Translate scientific and business complexity into scalable model logic and reusable ...
• Context- and Knowledge-Driven Data Modelling.
• Develop context-rich data models that connect scientific data, metadata, documents, protocols, business rules, and domain concepts into reusable knowledge assets.
• Create information structures that preserve scientific meaning and operational context to improve consistency across functions and over time.
• Improve discoverability and reuse by formalizing relationships, definitions, and contextual attributes across fragmented systems and data sources.
• Ontology, Knowledge Graph, and RAG Foundations.
• Apply ontology principles to define consistent concepts, hierarchies, relationships, and machine-readable rules across priority R&D data domains.
• Support the development of R&D knowledge graph foundations by modeling relationships between experiments, protocols, observations, methods, assets, and decisions.
• Enable Retrieval-Augmented Generation (RAG) and other knowledge-driven AI approaches by improving retrieval structures, contextual linkages, and connections between structured and unstructured information.
• AI-Ready Data Platform Enablement.
• Contribute to AI-ready data platforms by defining reusable knowledge layers, integration patterns, and data-readiness standards.
• Partner with platform owners, Bioinformatics leads and technical stakeholders to scalable AI integration, and reliable information retrieval.
• Collaboration with Data Scientists and Bioinformatics Leads.
• Collaborate with Data Scientists and Bioinformatics lead to ensure analytical, digital, and AI solutions are built on reusable and scalable data foundations.
• Contribute to shared architecture discussions, design reviews, and foundational modelling decisions aligned with business and platform needs.
• Documentation, Standards, and Change Enablement.
• Document modelling standards, ontologies, schemas, and reusable reference patterns to support consistent adoption across teams.
• Provide technical guidance on computational data models, ontology structures and AI-ready data design approaches.
• Governance, Safety, and Professional Standards.
• Ensure data models and knowledge structures align with governance, security, lineage awareness, traceability, and responsible AI enablement standards.
• Balance architectural rigor, usability, innovation, usability, and practical business value to support scalable implementation.