Associate Director, FAIR Data Operations

Role Summary

Associate Director, FAIR Data Operations. Lead the strategy and execution of FAIR data operations across oncology biomarkers, translational science, clinical development, and real-world evidence. Own the operating model for end-to-end data flows to deliver scalable, compliant, high-quality data products. Report to the Director, Data Products, Ops & Governance.

Responsibilities

• Strategy and Operating Model: Define the FAIR data roadmap, OKRs, and standards for ingestion, curation, harmonization, and provisioning across multi-omics, imaging, clinical, and RWE; drive enterprise metadata and ontology adoption.
• Leadership and People Management: Build and develop a high-performing team; lead cross-functional squads and foster continuous improvement.
• Enterprise Data Flow Ownership: Design and oversee primary data flows, monitoring, SLOs, incident/problem management, documentation, lineage, and catalog-driven access.
• Standards, Compliance, and Governance: Implement data/metadata standards and controlled vocabularies; embed privacy-by-design and regulatory compliance (e.g., GDPR).
• Data Productization and Automation: Partner with data product teams to deliver reusable FAIR assets, accelerate curation, and capture metadata at source.
• Stakeholder Management and Change: Translate scientific needs into requirements, run governance cadences, communicate risk/value, and champion agile/DataOps ways of working.

Qualifications

• Master's degree in Data Science, Bioinformatics, Computational Biology, Life Sciences; PhD preferred.
• 5+ years' experience in data management/operations within Life Sciences/Pharma R&D and 2+ years leading teams/programs.
• Proven scale-up of FAIR practices across complex R&D data (digital pathology, genomics, -omics, clinical, RWE).
• Technical: Unix/Python familiarity, workflow orchestration, standards/ontology implementation, data privacy/compliance, and strong stakeholder communication.

Skills

• Ways of Working/Tools: Agile (Scrum/Kanban), Jira/Confluence; data observability/quality tooling; cloud data platforms, catalogs, lineage.
• Ontologies/References: Gene Ontology, NCI Thesaurus, EFO, HPO, MeSH, BAO; NCBO BioPortal, EBI OLS; UniProt, Ensembl, ChEMBL, EntrezGene, ClinicalTrials.gov.
• Clinical Standards: CDISC SDTM/ADaM; FHIR/OMOP for RWE integration; Power BI/PowerQuery for operational insights.

Education

• Master's degree in Data Science, Bioinformatics, Computational Biology, Life Sciences; PhD preferred.