For Oncology R&D data domains, model, code, test, and release ontology modules and mappings that power knowledge graph, master/reference data, analytics, and AI/LLM applications.
Translate domain knowledge from Oncology & subject-matter experts in related disciplines into OWL/RDF classes and properties, SKOS vocabularies, and SHACL constraints, and publish validated, versioned packages and APIs for broad reuse.
Participate in a wider community of scientists standardizing biomedical data across R&D aimed at increasing AI Readiness of a broad range of data domains Maintain accountability for selected Oncology R&D ontology roadmap for key domains – define scope, use cases, and success metrics; align with internal canonical models and relevant W3C/domain standards.
Model and govern the ontology using (OWL, RDFS, SKOS, SHACL); capture provenance and maintain SPARQL queries with validation.
Adhere to standards—modeling guidelines, naming/versioning, change control, releases/deprecations—and partner with enterprise governance/tooling teams to embed reusable patterns for product teams and consumers.
Play a role in developing these standards as appropriate.
Implement data quality checks (coverage, conformance, unit/ID normalization) and contribute to validation reports.

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.
3-5+ years of hands-on ontology/knowledge modeling, taxonomy design, and metadata standards.
Proficiency with OWL, RDF(S), SKOS, SHACL, SPARQL, ontology design patterns and reasoning concepts.
Familiarity large-scale knowledge graphs construction, ontology development and pharmaceutical or healthcare domains integration.
Proficiency in semantic web technologies (SPARQL, RDF, OWL), familiarity with graph databases (Neo4j, Amazon Neptune, GraphDB).
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.
Proven record leading improvement initiatives with multi-disciplinary and remote partners.
Demonstrated stakeholder management capabilities
including requirements gathering, business analysis and planning.
Must have the capacity to translate discussions into user requirements and project plans.
Ability to manage a numerous projects simultaneously, prioritize work, exhibit organizational skills and flexibility to deliver maximum business value.
Willingness to conduct periodic travel (<15% of time) to conferences and internal meetings.

Senior Data Scientist – Oncology

Key skills