Scientific Knowledge Engineer, Ontology & Data Modeling

deliverable business requirements that enable the integration of large-scale biology data to predict, model, and stabilize therapeutically relevant protein complex and antigen conformations for drug and vaccine discovery. - Collaboration with external groups to align data standards with industry/academic ontologies, ensuring that data standards are defined with usage/analytics in mind. - Providing bespoke subject-matter expertise for R&D data to translate deep science into data for actionable insights. - Contributing to and maintaining documentation of data standards, ontology decisions, and mapping rationale to support organizational knowledge transfer and auditability. Basic Qualifications - Master's degree in Bioinformatics, Biomedical Science, Biomedical Engineering, Molecular Biology, or Computer Science (with a life-science application focus). - 6+ years of relevant work experience. - Specific experience contributing to Knowledge Graph development efforts, including entity modeling, relationship design, and schema governance. - Hands-on experience with open-source ontology tools and languages: Protégé, SPARQL, OWL, SKOS, SHACL, RML, RDF/Turtle. - Working knowledge of major life-sciences ontologies: Gene Ontology (GO), OBO Foundry ontologies (CL, UBERON, HPO, MONDO, CHEBI, EFO, CLO), MeSH, SNOMED CT, UMLS. - Familiarity with linked-data principles and semantic-web technologies. - Experience with industry-standard tools for building data serialization protocols (e.g., JSON Schema, LinkML). - Proficiency in at least one programming language-preferably Python-for scripting vocabulary mappings, building data models, automating QC, and prototyping pipelines. Preferred Qualifications - Experience with data governance and data quality tooling (e.g., Ataccama, Informatica, Talend, OpenRefine, Great Expectations, dbt). - Experience supporting LLM integration or AI-readiness workflows-including metadata enrichment, entity linking, embedding pipelines, or retrieval-augmented generation (RAG) architectures. - Understanding of vector databases and their role in semantic search and knowledge retrieval (e.g., Weaviate, Chroma). - Familiarity with cloud data platforms and infrastructure relevant to large-scale biological data (e.g., AWS, GCP, Azure). - Familiarity with graph database technologies (e.g., Neo4j, Amazon Neptune, Stardog, GraphDB, TigerGraph). Compensation Salary Range: €58,000 - €68,000 gross per year, depending on experience, skills, and overall fit for the role. J-18808-Ljbffr