Data Modeler

Role: Data Modeller / Senior Data Modeller

Location: India (Remote)

Contract

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 vocabularies
• Ability 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.

Senior-level expectations:

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

Desirable specialist tooling and modelling experience:

Candidates should ideally have experience with LinkML or equivalent schema modelling frameworks; YAML-based schema authoring; biomedical ontology and controlled vocabulary tooling; ontology resources ,knowledge graph modelling; schema/model registry tooling; data catalogue or metadata registry tooling; data dictionary management; lineage and provenance modelling; schema validation and data quality tooling; source-to-canonical mapping approaches; FAIR metadata assessment; and query-based validation using SQL or Python/notebooks where specifically applied to model conformance, ontology mapping or data quality checks.