Job description

Job Description Summary GE Vernova's Gas Power Data Science organization is building an enterprise ontology and knowledge graph capability to give AI agents, GenAI applications, and advanced analytics a shared, governed representation of the business. We're looking for a Senior Ontology & Knowledge Graph modeling to own the ontology and knowledge graph strategy across all of Gas Power including services, engineering, and commercial domains, ensuring they interoperate as one coherent semantic layer rather than a set of individually correct but disconnected models. You'll build the semantic "connective tissue" that lets Gas Power derive intelligence from its industrial data, leading the shift from traditional data management to an AI-ready semantic ecosystem that enables advanced diagnostics, predictive maintenance, and strategic decision-making. This is a program level role. You remain technically hands on enough to build and debug models when it matters, but your time is weighted toward setting modeling standards, resolving cross-domain conflicts, and acting as the final technical authority on ontology. You own how the organization scales its model-building capability training approach, and delivery process for contractors, FDEs (Forward Deployed Engineers), and SMEs across the entire program, and you are accountable for the organization's overall ontology capability, not just the output of any single domain. You will define and own the semantic model governance framework end to end, versioning policy, lifecycle, access model, and how governance holds up as adoption scales, and you will own the platform selection decision and long-term roadmap. Job Description Roles and Responsibilities Own the ontology and knowledge graph strategy across all Gas Power domains: services, engineering, and commercial, ensuring domain models interoperate as one coherent semantic layer. Set enterprise-wide modeling standards and patterns (OWL 2, RDF/RDFS, SHACL, SPARQL) that all domain models must follow. Resolve cross-domain modeling conflicts and serve as the final technical authority when domain models compete or overlap. Design and own the organization s model for scaling ontology work: how contractors, FDEs, and SMEs are staffed, trained, and deployed across the full program. Build and evolve the training curriculum that brings SMEs to self-sufficiency in building and maintaining their own domain models. Own the organization s overall ontology building capability, measured not by any single domain s output, but by how well the program scales without bottlenecking on any one person. Define the model approval framework: review criteria, review board/process, and escalation path for contested or high-risk models. Serve as final arbiter on contested or high-risk model approvals, while delegating day-to-day approvals within the defined framework. Own the semantic model governance framework end to end: versioning policy, model lifecycle, access model, and metadata/lineage management. Ensure governance scales as the knowledge graph grows, auditing for drift, inconsistency, and standards erosion across domains. Partner with the GenAI/ML and AI platform teams to ensure the knowledge graph is the grounding layer for RAG and agentic workflows program-wide. Communicate modeling trade-offs, and delivery progress clearly to technical teams and senior stakeholders. Contribute reusable standards, reference documentation, and mentoring that strengthen ontology and semantic modeling capability across the team. Required Qualifications 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 the integration patterns required to connect semantic layers to enterprise data products. Demonstrated ability to independently deliver a defined technical scope within a broader architecture and product roadmap. Strong communication and stakeholder-management skills, with the ability to convert ambiguous requirements into clear semantic models and recommendations. Demonstrated ability to work independently on a defined scope while collaborating within a broader technical roadmap. Preferred Qualifications Experience in an industrial, energy, or manufacturing environment (services demand, asset performance, or engineering data domains a plus). Exposure to GenAI/LLM applications, particularly RAG architectures grounded in structured knowledge. Familiarity with MCP (Model Context Protocol) or similar agent-to-data integration patterns. Experience contributing to platform evaluation or architecture decision documents for executive audiences. Knowledge of data governance, metadata management, or MLOps practices (MLflow or similar). Additional Information GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer . Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law. GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable). Relocation Assistance Provided: No For candidates applying to a U.S. based position, the pay range for this position is between $119,200.00 and $198,600.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set. Bonus eligibility: discretionary annual bonus. This posting is expected to remain open for at least seven days after it was posted on October 08, 2026. Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off. GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.

Requirements

Must-haves

Job Description Roles and Responsibilities
Own the ontology and knowledge graph strategy across all Gas Power domains: services, engineering, and commercial, ensuring domain models interoperate as one coherent semantic layer.
Set enterprise-wide modeling standards and patterns (OWL 2, RDF/RDFS, SHACL, SPARQL) that all domain models must follow.
Resolve cross-domain modeling conflicts and serve as the final technical authority when domain models compete or overlap.
Design and own the organization s model for scaling ontology work: how contractors, FDEs, and SMEs are staffed, trained, and deployed across the full program.
Build and evolve the training curriculum that brings SMEs to self-sufficiency in building and maintaining their own domain models.
Own the organization s overall ontology building capability, measured not by any single domain s output, but by how well the program scales without bottlenecking on any one person.
Define the model approval framework: review criteria, review board/process, and escalation path for contested or high-risk models.
Serve as final arbiter on contested or high-risk model approvals, while delegating day-to-day approvals within the defined framework.
Own the semantic model governance framework end to end: versioning policy, model lifecycle, access model, and metadata/lineage management.
Ensure governance scales as the knowledge graph grows, auditing for drift, inconsistency, and standards erosion across domains.
Partner with the GenAI/ML and AI platform teams to ensure the knowledge graph is the grounding layer for RAG and agentic workflows program-wide.
Communicate modeling trade-offs, and delivery progress clearly to technical teams and senior stakeholders.
Contribute reusable standards, reference documentation, and mentoring that strengthen ontology and semantic modeling capability across the team.
Required Qualifications 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.

Nice-to-haves

Experience in an industrial, energy, or manufacturing environment (services demand, asset performance, or engineering data domains a plus).
Exposure to GenAI/LLM applications, particularly RAG architectures grounded in structured knowledge.
Familiarity with MCP (Model Context Protocol) or similar agent-to-data integration patterns.
Experience contributing to platform evaluation or architecture decision documents for executive audiences.
Knowledge of data governance, metadata management, or MLOps practices (MLflow or similar).
GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
Relocation Assistance Provided: No
This posting is expected to remain open for at least seven days after it was posted on October 08, 2026.

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Platform: workday
Effort: long

Benefits

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Dental

Vision

401(K)

Parental Leave

Professional Development