Knowledge Graph Engineer, Data Science – Digital Health, Data Strategy and Products

Role Overview

• 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.

Tech Stack

• Azure
• Docker
• GraphQL
• Jenkins
• Neo4j
• SQL

Benefits

• Inclusive work environment
• Professional development opportunities