Senior Data Modeller

YOUR ROLEYou will lead a modelling strategy for scientific data harmonization work.
This work aims to strengthen mechanistic understanding of disease and make integrated FAIR data products available to R&D scientists for rapid query and decision-making.
You turn ambiguous scientific requirements into clear canonical models, make and document ontology decisions, and set the standards for identifiers, provenance and schema governance across data assets.
Lead modelling strategy across data harmonization, pipeline validation, knowledge graph and FAIR data product requirements
Translate ambiguous scientific requirements into clear conceptual, logical and canonical data models
Model biomedical entities and relationships including genes, targets, diseases, indications, phenotypes, mechanisms, biomarkers, tissues, cell types, assay modalities, evidence, provenance and causal relationships
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 in GitHub, including changelogs, tagged releases and downstream impact assessment
Define validation rules and data quality checks for ontology terms, ranges, required fields, ID and label consistency, cross-field consistency and provenance completeness
Identify modelling risks early: metadata gaps, ontology conflicts, source data quality issues, lineage gaps and downstream compatibility problems
Design modular, reusable and future-proof models aligned to FAIR principles and enterprise standards
Work with scientific knowledge engineering, biology SMEs, data engineering, knowledge graph engineering, product management, data stewards and platform teams
Mentor junior and mid-level modelers
YOUR PROFILEMSc or PhD (or equivalent professional experience) in biology, biomedical sciences, bioengineering, genetics, neuroscience, bioinformatics, computational biology or a related field
More than 6 years of experience in scientific or biomedical data modelling, data harmonization or knowledge engineering, with senior ownership
Strong conceptual, logical and canonical modelling experience for complex biomedical or scientific domains
Practical, in-depth experience with LinkML or an equivalent schema framework: classes, slots, ranges, identifiers, required fields, constraints, cardinality, descriptions and ontology bindings
Strong data harmonization experience: source-to-canonical mapping, controlled vocabulary alignment, persistent identifiers, lineage and provenance
Proven experience defining validation rules and data quality checks: ontology term validity, ranges, required fields, ID and label consistency, cross-field consistency and provenance completeness
Strong understanding of FAIR principles, metadata standards, provenance and schema versioning
Strong command of biomedical ontologies and vocabularies such as HGNC, Ensembl, UniProt, MONDO, Disease Ontology, HPO, EFO, UBERON, Cell Ontology, Gene Ontology, Reactome, KEGG, ChEBI, OBI, LOINC, UCUM, ECO, SEPIO, Relation Ontology and PATO
Experience managing the schema lifecycle with GitHub-based repositories, semantic versioning, changelogs, data dictionaries and metadata catalogues
Experience designing models that support pipelines, APIs, knowledge graphs, FAIR data products and analytical workflows
Solid Python and a working command of SQL, used for validation, mapping checks and exploration of real biomedical datasets
A public or shareable repository (GitHub, GitLab or similar) demonstrating schema and governance work.
Please include the link in your application
Excellent stakeholder management and communication;
Fluent English
Preferred QualificationsSemantic modelling technologies: RDF, OWL, JSON-LD, SHACL, ShEx and SPARQL;
knowledge graph modelling
Schema or model registry, data catalogue and metadata registry tooling
Lineage and provenance modelling, and FAIR metadata assessment
Query-based validation using SQL or Python and notebooks for model conformance, ontology mapping or data quality checks
Agentic AI or LLM-assisted mapping and curation, with robust validation
Experience in pharmaceutical R&D or another regulated research environment
Cloud platforms, Docker and workflow orchestration
Applied machine learning and/or broad data science
Experience mentoring or leading a small technical team
Scientific curiosity paired with engineering discipline
Critical thinking and attention to data provenance and quality
Comfort with ambiguity, and the ability to move between scientific meaning and technical implementation
An impact-driven mindset in a fast-paced environment
Portuguese fluency
What You ́ll Love About Working HereJoin a multicultural and inclusive team environment.
Enjoy a su