Director, Research Data Foundation

pAt Johnson Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions 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. /p pAs 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. /p h3Job Function /h3 pData Analytics Computational Sciences /p h3Job Sub Function /h3 pData Engineering /p h3Job Category /h3 pPeople Leader /p h3All Job Posting Locations /h3 pBeerse, Antwerp, Belgium /p h3Job Description /h3 pWe are searching for the best talent for a Director, Research Data Systems to be located in Spring House, PA; Raritan, NJ; Titusville, NJ or Beerse, Belgium. /p pPlease note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s): /p h3United States
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- /h3 pRemember, whether you apply to one or all of these requisition numbers, your applications will be considered as a single submission. /p pThe Director, Research Data Systems, Innovative Medicines RD 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. /p pThis 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. /p pThe 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. /p h3Key Responsibilities /h3 h3Research Data Ingestion and Curation Services /h3 ul liLead scalable data ingestion and curation platforms, covering onboarding, metadata enrichment, pipeline engineering, orchestration, monitoring, and issue resolution. /li liEnable tools to standardise naming conventions, identifiers, provenance, lineage, ontologies, vocabularies, quality thresholds, stewardship, sharing, and compliant scientific reuse. /li liPartner with data product teams and scientists to ensure scientific context and data quality. /li liEnable governed and performant access to distributed RD data through scalable integration and data virtualization, including federation, semantic access, security, caching, and performance optimization. /li /ul h3Research Data Quality, Governance and FAIR Standards /h3 ul liDefine and operate research data quality and FAIR frameworks, standards, thresholds, and executive reporting across priority research data domains. /li liPartner with data owners, stewards, DDSAI, product teams, and business functions to establish quality rules, ownership, metadata expectations and FAIR acceptance criteria. /li liEstablish consistent, automated monitoring and governance across diverse research data, covering quality and completeness. /li liTranslate 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. /li /ul h3Research Data Semantics, Metadata and Master Data /h3 ul liLead research data catalog, metadata, ontology, lineage, and master/reference data capabilities to improve the findability, context, interoperability, governance, and reuse of research data. /li liEstablish consistent adoption of canonical research entities, identifiers, controlled vocabularies, semantic standards, metadata, lineage,