Data Scientist

• Own the ontology and knowledge graph strategy across Gas Power services, engineering, and commercial domains
• Set enterprise-wide modeling standards and patterns using OWL 2, RDF/RDFS, SHACL, and SPARQL
• Resolve cross-domain modeling conflicts and serve as final technical authority
• Design and own the model for scaling ontology work across contractors, FDEs, and SMEs
• Build and evolve training curriculum to enable SMEs to build and maintain domain models
• Own the organization’s overall ontology-building capability and scaling approach
• Define the model approval framework, review criteria, review process, and escalation path
• Serve as final arbiter on contested or high-risk model approvals
• Own semantic model governance, including versioning, lifecycle, access, metadata, and lineage management
• Audit for drift, inconsistency, and standards erosion as the knowledge graph grows
• Partner with GenAI/ML and AI platform teams to make the knowledge graph the grounding layer for RAG and agentic workflows
• Communicate modeling trade-offs and delivery progress to technical teams and senior stakeholders
• Contribute reusable standards, reference documentation, and mentoring

Explore Senior Staff Data Scientist, Ontology Modeling at GE Vernova. Review requirements and application details.

Senior Staff Data Scientist, Ontology Modeling

• Own the ontology and knowledge graph strategy across Gas Power’s services, engineering, and commercial domains
• Set enterprise-wide modeling standards and patterns using OWL 2, RDF/RDFS, SHACL, and SPARQL
• Resolve cross-domain modeling conflicts and serve as final technical authority on ontology
• Design and own the model for scaling ontology work across contractors, FDEs, and SMEs
• Build and evolve training curriculum to bring SMEs to self-sufficiency in building and maintaining domain models
• Own the organization’s overall ontology-building capability
• Define the model approval framework, review criteria, review process, and escalation path
• Serve as final arbiter for contested or high-risk model approvals
• Own semantic model governance, including versioning, lifecycle, access, metadata, and lineage management
• Audit for drift, inconsistency, and standards erosion as the knowledge graph grows
• Partner with GenAI/ML and AI platform teams so the knowledge graph grounds RAG and agentic workflows
• Communicate modeling trade-offs and delivery progress to technical teams and senior stakeholders
• Contribute reusable standards, reference documentation, and mentoring