Senior Data Modeller

YOUR ROLE You 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 PROFILE MSc 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 qualifications  Semantic 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 HERE Join a multicultural and inclusive team environment.  Enjoy a supportive atmosphere promoting work life balance.  Hybrid work.  Your career growth is central to our mission. Our array of career growth programs and diverse professionals are crafted to support you in exploring a world of opportunities.  Access valuable training and certifications in cutting edge technologies.  Engage in exciting national and international projects.  Health and life insurance.  Referral program with bonuses for talent recommendations.  Great office locations.  ABOUT CAPGEMINI ABOUT CAPGEMINI Capgemini is the business transformation partner for enterprises in the age of AI. We help organizations imagine and build an intelligent, sustainable future, combining AI, technology and human ingenuity to transform how they operate, innovate and grow. With unique end-to-end capabilities spanning strategy, technology, engineering and intelligent operations, we bring together deep industry expertise and market-leading capabilities in AI, cloud and data to turn ambition into measurable business outcomes at scale. Supported by a robust ecosystem of partners and nearly 60 years of expertise, Capgemini is a responsible and diverse global organization of over 410,000 team members in more than 50 countries.