The Computational Data Modeler plays a critical role in strengthening Crop Protection R&D by designing reusable data and knowledge structures that make complex scientific information interoperable, discoverable, and AI‑ready. By translating domain complexity into robust computational models and knowledge foundations, the role enables predictive data analytics, scalable digital products, and next‑generation AI solutions across the region – AMEA & JANZ

Accountabilities:

Data Engineering and Computational Modelling

• 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 data structures that support analytics, digital workflows, and AI-enabled applications.

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