Senior Staff Data Scientist, Ontology Modeling

Job 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, Protege, 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).