Data Scientist designing and delivering analytical models for casualty catastrophe risks in Boston or Jersey City.
JJ
Hybrid Knowledge Graph Engineer, Data Science – Digital Health, Data Strategy and Products
Posted 10 hours ago
About the role
- Knowledge Graph Engineer at Johnson & Johnson focusing on biomedical data standardization and interoperability solutions. Contributing to healthcare innovation through data-driven technologies and collaborations.
Responsibilities
- Contribute to the design and implementation of a scalable knowledge graph infrastructure focused on data standardization and interoperability.
- Curate and extend ontologies for clear mapping into established biomedical ontologies and controlled terminologies using RDF standards.
- Apply graph-based data modeling for efficient organization, integration and retrieval to ensure system flexibility and long-term maintainability.
- Stand up SPARQL/GraphQL/REST services; develop ingestion and curation pipelines to ingest, normalize and map concepts across data sources.
- Extend and curate ontologies (e.g., diseases, drugs, targets, pathways, etc.) and maintain synonyms, cross-references, and provenance.
- Partner with cross-functional teams to enable NLP/RAG over graphs, features for predictive modeling and terminology services for search and study design tools.
- Work with IT and DevOps teams to deploy and manage the graph database infrastructure, focusing on high availability, scalability, and recovery operations.
- Create and be responsible for documentation, such as data dictionaries, data lineage, and data flow diagrams, to facilitate understanding of the knowledge graph.
Requirements
- Desired Ph.D. or master's degree in bioengineering, computer science, IT, bioinformatics, physics, mathematics, or related fields, emphasis on semantic technologies and biomedical application.
- At least 5 years professional experience in health informatics, or at least 7 years of professional experience or with additional consideration for candidates with graduate degrees or equivalent experience.
- Programming background in parser combinators, natural language processing, and linked data (RDF Triple Stores and property graphs).
- Demonstrated experience in large-scale knowledge graphs construction, ontology development, pharmaceutical or healthcare domains integration.
- Proficiency in semantic web technologies (SPARQL, RDF, OWL), familiarity with graph databases (Neo4j, Amazon Neptune).
- Proven work with complex biomedical datasets, including genomics, proteomics, and high-throughput screening data.
- Impressive records in a pharmaceutical, biotech, or related research environment are preferred.
- Proficiency in various data storage solutions (SQL, key-value, column, document, graph stores) and data modeling techniques (semantic data, ontologies, taxonomies).
- Experience in CI/CD implementations, git usage, CI/CD stacks (Jenkins, GitLab, Azure DevOps), DevOps tools, metrics/monitoring, and containerization technologies (Docker, Singularity).
- Strong skills in analysis, problem-solving, organizational change, project delivery, and managing external vendors.
- Demonstrated agile decision-making, performance management, continuous learning, and commitment to quality.
- Ability to multi-task, prioritize work, exhibit organizational skills and flexibility to deliver maximum business value.
- Capacity to translate discussions into user requirements and project plans.
- Willingness to travel less than 25% to conferences and internal meetings.
Benefits
- Inclusive work environment
- Professional development opportunities
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