Data Modeler

We require Data Modeller(s) / Senior Data Modeller(s) with strong experience in scientific, biomedical or research data modelling, with life sciences, translational research, causal biology, genetics, disease biology, knowledge engineering, data harmonisation or regulated research data environments. Experience range is 10+ years.

Mandatory skills

• :Strong conceptual, logical and canonical data modelling experience for complex biomedical or scientific domains
• .Strong data harmonisation experience, including source-to-canonical mapping, controlled vocabulary alignment, persistent identifiers, lineage and provenance
• .Practical experience with LinkML or equivalent schema modelling frameworks, including classes, slots, ranges, identifiers, required fields, constraints, cardinality, descriptions and ontology bindings
• .Strong understanding of FAIR data principles, including findability, accessibility, interoperability, reusability, persistent identifiers, metadata standards, provenance and schema versioning
• .Experience with biomedical ontologies and controlled vocabularie
• sAbility to define validation rules and data quality checks, including ontology term validation, range checks, required field checks, ID/label consistency, cross-field consistency and provenance completeness
• .Ability to design models that support pipelines, APIs, knowledge graphs, FAIR data products, analytical workflows and downstream R&D query use cases
• .Experience managing schema lifecycle, including GitHub-based schema repositories, semantic versioning, changelogs, tagged releases, data dictionaries, metadata catalogues and downstream impact assessment
• .Ability to work with Scientific Knowledge Engineering, Causal Biology SMEs, Data Engineering, Knowledge Graph Engineering, Product Management, Data Stewards and Platform teams

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Senior-level expectation

• s:Lead modelling strategy across data harmonisation, pipeline validation, knowledge graph and FAIR data product requirement
• s.Translate ambiguous scientific requirements into clear canonical data model
• s.Make ontology reuse, extension and mapping decisions, with documented rational
• e.Define persistent identifiers and consistent provenance fields across data asset
• s.Drive schema review, approval, versioning and publication processe
• s.Identify modelling risks early, including metadata gaps, ontology conflicts, source data quality issues, lineage gaps and downstream compatibility risk
• s.Design modular, reusable and future-proof models aligned to FAIR principles and enterprise standard