INTL Virology Data Steward

Required Skills & Experience

• Bachelor’s degree or higher in Biology, Bioinformatics, Computational Biology, Data Science (with strong life-sciences focus), or a related field. -Hands-on experience with data stewardship and/or data governance concepts (e.g., stewardship operating model, data quality, metadata, ownership, and standards).
• Experience working with data standards, ontologies, vocabularies, or controlled terminologies in a research or life-sciences setting. -Familiarity with laboratory workflows, assay data, omics data, or scientific research systems.
• Experience working with LIMS, ELN, or scientific data platforms.
• Working knowledge of data modeling and structuring datasets for analysis and reuse.

Nice to Have Skills & Experience

• Familiarity with standardization that supports analytics and visualization tooling.
• Experience working with public or licensed external datasets, including interpretation/management of terms of use, data sharing restrictions, and related governance controls.
• Experience with vaccines data and in vitro and in vivo preclinical assays.
• Experience working with biobanks and/or other external, public research datasets and enabling cross-source integration.
• Experience building or curating consolidated datasets and standardizing data types across programs.

Job Description

One of our large Pharmaceutical clients is actively looking for a Virology Data Steward to join their team. This role supports the VIDRU Discovery Data Infrastructure (DDI) program by driving data stewardship and data ownership across Research and Exploratory Clinical Research (ECR) datasets. The consultant will establish and promote data standards (ontologies, vocabularies, metadata), enable consistent modeling and integration of internal and purchased/public datasets, and oversee governance that helps multiple research data workflows to scale safely and consistently (e.g., multi-omics, sample/asset metadata, research data warehousing). Work includes supporting multiple modalities of therapeutics (ADCs, mRNA, Protein Antigens, Polysaccharides & MAPS) and experimental frameworks for response data to fully enable data re-use, FAIR adoption and active learning by using AI/ML at scale.

• Serve as a data steward for vaccine and infectious disease research datasets, ensuring data is well-defined, governed, and usable across the organization (this is not a clinical data role).
• Define, standardize, and promote controlled vocabularies, ontologies, and data standards for research (including pathogen, multi-omics, immunology, molecular design, structural biology, and preclinical data).
• Support integration and readiness of internal datasets for combined datasets in enterprise systems.
• Partner with data and lab scientists, data engineering teams (Tech), and platform stakeholders to ensure delivery with embedded vocabularies, ontologies and data standards and linking of assets and metadata. This role will contribute to the team effort by providing data standard expertise and not independently deliver solutions.
• Contribute ontology and vocabulary expertise to data modeling efforts and standardization that support downstream visualization and reporting tools.
• Drive stakeholder engagement and influence: communicate standards clearly, advocate for adoption, and coordinate across teams to reduce fragmentation and improve consistency.
• Support alignment across multiple research “tech builds” by defining governance, metadata, and standards that enable consistent capture and reuse in the GSK research ecosystem (e.g., large molecule design initiatives, lab equipment data pipelines, and sample management/collection: what samples are, how they are used, and how they are traced).
• Partner with engineering teams (Tech) to enable solutions for research covering pathogens, in-vitro and animal in-vivo assays, human biological sample assays (ECR only), and related model systems.
• Support scaling of datasets by helping govern a large warehouse of collected data, ensuring consistent structure, metadata, lineage, and quality controls.
• Define and operationalize guidance for data terms of use (licenses, permitted use, sharing restrictions, retention) and set/communicate policy expectations for research teams consuming public and internal data.
• Classify datasets and use-cases into high / medium / low risk categories and ensure appropriate governance controls are in place (e.g., access expectations, review steps, and documentation).
• Document project artifacts and contribute to milestone-based deliverables in alignment with the SOW engagement model.