Associate Director, FAIR Data Operations

The Oncology Data Science – Data, Analytics, and AI Platforms (ODSP) team comprises multiple groups across the Cambridge UK, Boston MA, Gaithersburg MD, and Munich DE sites. ODSP develops technology, software, and data infrastructure to advance our Oncology data science. Our vision is to evolve data science into a driver of innovation within Oncology. We do this by creating a strategic culture that values our data as a true business asset, organizing our data so that it’s accessible and easy to find, integrated, and re-usable for Humans & AI, and building the best computational tools to support scientific decision making.

The Oncology Data Science Platforms Team is delivering the FAIR data foundation of clinical, molecular and biosamples data to the Oncology data consumers (scientists, bioinformaticians, data scientists etc.). As the Associate Director, FAIR Data Operations, you will lead the strategy and execution of FAIR (Findable, Accessible, Interoperable, Reusable) data operations across oncology biomarkers, translational science, clinical development, and real-world evidence. You will own the operating model for end-to-end data flows to deliver scalable, compliant, high-quality data products. Partner with scientific, clinical, engineering, and governance leaders to embed FAIR-by-design and strengthen cross-functional data culture. You will report to the Director, Data Products, Ops & Governance and may be based in Waltham, MA or Gaithersburg, MD.

Key Accountabilities

• 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.

Minimum 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.

Preferred 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.