Principal Data Scientist - Immunology - (2 Positions)

Johnson & Johnson Johnson & Johnson · Pharma · Titusville, NJ +7

The Principal Data Scientist - Immunology will design and implement a scalable knowledge graph infrastructure to standardize and connect biomedical and clinical data for Immunology R&D. This role involves applying graph-based data modeling, curating ontologies, working with semantic web technologies, and enabling NLP/RAG over graphs to power analytics and AI across the organization.

What you'd actually do

  1. Be a key contributor to the design and implementation of a scalable knowledge graph infrastructure focused on data standardization and interoperability, focusing on Immunology R&D data.
  2. Apply graph-based data modeling for efficient Immunology R&D organization, integration and retrieval to ensure system flexibility and long-term maintainability.
  3. Work with a larger community of Data Scientists, Clinical Scientists, and Discovery Scientists to standardize, curate and create AI-Ready data sets.
  4. Curate and extend ontologies for clear mapping into established biomedical ontologies and controlled terminologies using resource description framework (RDF) standards.
  5. Work with SPARQL/GraphQL/REST services; develop ingestion and curation pipelines to ingest, normalize and map concepts across data sources.

Skills

Required

  • Knowledge graph infrastructure design and implementation
  • Graph-based data modeling
  • Ontology curation and extension
  • SPARQL/GraphQL/REST services
  • Data ingestion and normalization pipelines
  • Resource Description Framework (RDF) standards
  • Biomedical and clinical data integration

Nice to have

  • Ph.D. or master's degree in bioengineering, computer science, IT, bioinformatics, physics, mathematics, or related fields
  • Health informatics experience
  • Large-scale knowledge graphs construction
  • Pharmaceutical or healthcare domains integration
  • Parser combinators
  • Natural language processing
  • Linked data (RDF Triple Stores and property graphs)
  • Semantic web technologies (SPARQL, RDF, OWL)
  • Graph databases (Neo4j, Amazon Neptune)
  • Complex biomedical datasets (clinical, genomics, proteomics)
  • Various data storage solutions (SQL, key-value, column, document, graph stores)
  • Data modeling techniques (semantic data, ontologies, taxonomies)
  • CI/CD implementations
  • Git usage
  • CI/CD stacks (Jenkins, GitLab, Azure DevOps)
  • DevOps tools
  • Metrics/monitoring
  • Containerization technologies (Docker, Singularity)
  • Stakeholder management
  • Requirements gathering
  • Business analysis
  • Project planning
  • Managing multiple projects
  • Prioritization
  • Organizational skills
  • Flexibility

What the JD emphasized

  • Knowledge Graph Engineer
  • semantic technologies
  • ontology
  • graph data modeling
  • life sciences domain
  • AI-Ready data sets
  • NLP/RAG over graphs

Other signals

  • knowledge graph
  • ontology
  • semantic technologies
  • biomedical data
  • NLP/RAG