Director, Research Data Foundation

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

• Master’s degree or equivalent experience in Computer Science or Life Sciences; advanced degree preferred.
• 10+ years of progressive technology leadership experience with relevant focus.
• Solid experience in research data platform engineering, scientific data architecture, laboratory informatics, or enterprise research data management.
• Good background in designing and implementing cloud-based FAIR data environments, semantic and metadata capabilities, and enterprise master/reference data solutions.
• Experience in life sciences, healthcare, biotech, pharmaceuticals, or other highly regulated environments preferred.
• Good understanding of FAIR data principles, data product management, data contracts, research governance workflows, privacy, security, compliance, AI-ready data, and platform operating models.
• Shown to successfully lead multi-functional teams and influence research leaders and scientists.
• Outstanding communication skills with the ability to translate complex data platform and governance topics into clear business decisions and outcomes.

The Director, Research Data Systems, Innovative Medicines R&D is responsible for leading the research data technology capabilities that enable trusted, governed and reusable data across the Research organization. The role will drive a major transformation to bring research data from fragmented systems, instruments, labs, and (in silico) scientific workflows into a FAIR environment that accelerates discovery, translational science, analytics, AI/GenAI, and scientific decision-making.

This role partners across research functions, lab informatics teams, data product owners, scientists, data science/modeling teams, governance leaders, architecture, cybersecurity, and technology platform teams to run a research data operating model. The role translates research strategy, FAIR principles, scientific data needs and AI-readiness requirements into using scalable platforms, metadata practices, data services, standards, scorecards, and operating routines.

The role is accountable for research data platforms, research data ingestion, FAIR metadata and cataloging, scientific data quality and scorecards, research master/reference data, semantic interoperability, data virtualization, and research data products across Discovery, laboratory sciences, translational research, computational biology, chemistry, biologics, imaging, omics, screening, and other research domains.

• Lead scalable data ingestion and curation platforms, covering onboarding, metadata enrichment, pipeline engineering, orchestration, monitoring, and issue resolution.
• Enable tools to standardise naming conventions, identifiers, provenance, lineage, ontologies, vocabularies, quality thresholds, stewardship, sharing, and compliant scientific reuse.
• Partner with data product teams and scientists to ensure scientific context and data quality.
• Enable governed and performant access to distributed R&D data through scalable integration and data virtualization, including federation, semantic access, security, caching, and performance optimization.

• Define and operate research data quality and FAIR frameworks, standards, thresholds, and executive reporting across priority research data domains.
• Partner with data owners, stewards, DDSAI, product teams, and business functions to establish quality rules, ownership, metadata expectations and FAIR acceptance criteria.
• Establish consistent, automated monitoring and governance across diverse research data, covering quality and completeness.
• Translate quality and FAIR insights into remediation priorities and measurable improvement roadmaps, improving data readiness for analytics, AI/GenAI, computational modeling, knowledge graphs, translational science, and scientific reuse.

• Lead research data catalog, metadata, ontology, lineage, and master/reference data capabilities to improve the findability, context, interoperability, governance, and reuse of research data.
• Establish consistent adoption of canonical research entities, identifiers, controlled vocabularies, semantic standards, metadata, lineage, and provenance across research platforms, workflows, pipelines, and data products.
• Enable high-quality, semantically consistent research data across compounds, targets, assays, samples, studies, experiments, instruments, and other scientific domains to support analytics, AI, automation, knowledge management, and cross-study interoperability.

• Shown success building and operating enterprise-grade data solutions!
• Experience establishing operating models for governed, reusable data assets.
• Demonstrated ability to partner with scientists, data science, analytics, business, and technology organizations.
• Experience handling critical data services with reliability targets, operational metrics, issue remediation, cost optimization, and service improvement routines.
• Consistent track record driving adoption of shared platforms, common standards, self-service patterns, metadata practices, governance workflows, and data product lifecycle management.
• Strong executive influencing skills!