Senior Staff Data Scientist, Ontology Modeling

GE Vernova

. Own the ontology and knowledge graph strategy across Gas Power services, engineering, and commercial domains .

Posted 10/8/2026full-timeAtlanta • South Carolina • United StatesSenior💰 $119,200 - $198,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in ontology and knowledge graph strategy, with hands-on proficiency in OWL 2, RDF/RDFS, SHACL, and SPARQL. Capable of delivering data models and semantic technologies while effectively communicating with stakeholders and managing model governance.

Highest-signal resume keywords

Ontology Strategy OwnershipData Modeling ExpertiseProficiency in OWL 2, RDF/RDFS, SHACL, SPARQLKnowledge Graph DevelopmentStakeholder Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingSemantic TechnologiesKnowledge EngineeringOWL 2RDF/RDFSSHACLSPARQLModel GovernanceVersioning ManagementMetadata Management

Soft Skills

Strong CommunicationStakeholder ManagementIndependent WorkCollaboration

Tools & Technologies

Neo4jProtégéPalantir FoundryAtlanCloud Data Environments

Industry Keywords

EnergyManufacturingData GovernanceMLOpsGenAILLM Applications

Tech Stack

Tools & technologies

CloudNeo4j

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

Bachelor’s degree in computer science, information science, data science, engineering, or a related field, or equivalent practical experience
4–6 years of professional experience in data modeling, semantic technologies, or knowledge engineering, including at least 1–2 years working directly with ontologies
Professional experience delivering data models, semantic technologies, or knowledge engineering solutions, including direct work with ontologies or knowledge graphs
Hands-on proficiency with OWL 2, RDF/RDFS, SHACL, and SPARQL
Experience with at least one ontology, graph, or semantic modeling platform or tool, such as Neo4j, Protégé, Palantir Foundry, or Atlan
Working knowledge of modern cloud data environments and integration patterns connecting semantic layers to enterprise data products
Ability to independently deliver a defined technical scope within a broader architecture and product roadmap
Strong communication and stakeholder-management skills
Ability to convert ambiguous requirements into clear semantic models and recommendations
Ability to work independently on a defined scope while collaborating within a broader technical roadmap
Legally authorized to work in the United States
Successful completion of a drug screen, as applicable
Preferred: experience in an industrial, energy, or manufacturing environment
Preferred: exposure to GenAI/LLM applications, particularly RAG architectures grounded in structured knowledge
Preferred: familiarity with MCP or similar agent-to-data integration patterns
Preferred: experience contributing to platform evaluation or architecture decision documents for executive audiences
Preferred: knowledge of data governance, metadata management, or MLOps practices such as MLflow

Benefits

Comp & perks

Discretionary annual bonus
Medical, dental, vision, and prescription drug coverage
Health Coach from GE Vernova, a 24/7 nurse-based resource
Employee Assistance Program with 24/7 confidential assessment, counseling, and referral services
GE Vernova Retirement Savings Plan
Tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions
Fidelity resources and financial planning consultants
Tuition assistance
Adoption assistance
Paid parental leave
Disability benefits
Life insurance
12 paid holidays
Permissive time off
Professional development
Relocation assistance not provided