Key Responsibilities
Platform Assessment: Conduct a "health check" on current graph infrastructure (e.g., AnzoGraph, Neo4j, or Stardog).
Ontology & Schema Review: Evaluate existing schemas (RDF/OWL or Property Graph) for scalability and alignment with industry standards like MeSH, SNOMED, or UMLS.
Performance Optimization: Identify bottlenecks in ingestion pipelines and complex query execution (Cypher/SPARQL).
Strategic Roadmap: Define a "Way Forward" report including recommendations on build-vs-buy decisions, cloud migration, and integration with LLMs (GraphRAG).
Stakeholder Alignment: Translate technical graph concepts into value-driven insights for non-technical stakeholders in Research and Clinical teams.
Required Qualifications
Graph Expertise: 10+ years of experience with Graph Databases. Deep proficiency in LPG (Labeled Property Graphs) or RDF/Triple Stores.
Pharma Domain Knowledge: Proven experience handling biomedical data types (e.g., Gene-Disease associations, Chemical compounds, Patient journeys).
Semantic Web Standards: Strong understanding of Linked Data principles, URI strategies, and ontology modeling.
Data Engineering: Experience with ETL/ELT pipelines that feed graphs from unstructured (PDF publications) and structured (EDC, LIMS) sources.
Advanced Analytics: Experience implementing Graph Data Science algorithms (centrality, community detection) or integrating Graphs with Machine Learning.
Technical Stack Preferences
Graph DBs: AnzoGraph, Neo4j, Stardog,
Languages: Python, Java, SPARQL, Cypher, or Gremlin.
Bio-Ontologies: Familiarity with OBO Foundry, ChEMBL, or Ensembl.
Never miss an opportunity! Create an alert based on the job you applied for.
