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.). We are seeking an experienced data operations leader to own end-to-end stewardship of molecular and imaging data arising from clinical trials and related translational studies. This role will define and operationalize FAIR-by-design practices, establish rigorous data specifications, and lead data transfer agreements/processes with CROs, central labs, biomarker vendors, and imaging partners. The Associate Director will build a scalable operating model for ingestion, curation, harmonization, and provisioning across multi-omics and clinical imaging, ensuring quality, compliance, and scientific usability at speed. You will report to the Director, Data Products, Ops & Governance and may be based in Waltham, MA or Gaithersburg, MD.

Key Accountabilities

• Strategy and FAIR Operating Model:Define the FAIR data roadmap, OKRs, and pragmatic standards for ingesting, curating, harmonizing, and provisioning molecular and imaging clinical data; drive enterprise metadata, ontology, and catalog adoption to enable findability and reuse.

• Data Stewardship for Clinical Molecular & Imaging: Serve as accountable data steward for genomic, proteomic, and other -omics readouts from clinical trials and for digital pathology/imaging modalities (e.g., WSI, radiology DICOM); ensure modality-specific QC, metadata capture, and traceability from site/vendor to analysis environment.

• Vendor and Lab Integration: Lead the design and implementation of data specifications, data transfer agreements (DTAs), and SLAs with CROs, central labs, biomarker assay providers, and imaging vendors; standardize templates and acceptance criteria; oversee onboarding and performance monitoring.

• Standards, Compliance, and Governance: Implement data/metadata standards and controlled vocabularies across modalities; embed privacy-by-design and regulatory compliance (e.g., GDPR) and align with clinical data standards and internal governance cadences.

• Cross-Functional Delivery in Matrix Teams: Drive delivery across cross-functional matrix teams of data SMEs, data engineers, alliance/partnership managers, clinical operations, translational biomarker leads, imaging scientists, biostatistics, quality/compliance, privacy/legal, procurement/vendor management, and IT/security. Orchestrate work plans, dependencies, and acceptance criteria; champion agile/DataOps practices to ensure timely, compliant, and reusable outputs without direct line management responsibilities.

• Stakeholder Management and Change:Translate scientific and operational needs into clear requirements and delivery plans; communicate risk, value, and trade-offs; drive adoption of standards and tools through training and change management.

Minimum Qualifications

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

• Experience: 5+ years' experience in data management/operations within Life Sciences/Pharma R&D.

• Modality Expertise: Demonstrated stewardship of clinical molecular data (e.g., NGS, qPCR, proteomics) and imaging/digital pathology or radiology data within clinical trials, including QC pipelines, metadata capture, and compliance controls.

• FAIR at Scale: Proven track record implementing and scaling FAIR practices across complex R&D data (digital pathology, genomics/-omics, clinical) with measurable impact on data findability, interoperability, and reuse.

• Vendor/Lab Data Transfer: Hands-on experience defining data specs, mapping guides, transfer protocols, and DTAs; managing external partners and ensuring timely, quality-compliant data deliveries.

• Technical: Proficiency with Unix and Python, workflow orchestration (e.g., Airflow, Prefect), data modeling and standards/ontology implementation, data privacy/compliance, and strong stakeholder communication and documentation skills.

Preferred Skills