Senior Staff Data Scientist, Ontology Modeling

Description

The company's Gas Power Data Science group is building an enterprise ontology and knowledge graph so AI agents, GenAI applications and advanced analytics work from one governed model of the business.

This is a senior, program-level role that owns the ontology and knowledge graph strategy for Gas Power's services, engineering and commercial domains, so they form one connected semantic layer instead of separate models that are each correct but do not link up.

The work is to build the semantic layer that lets Gas Power draw insight from industrial data, moving the business from traditional data management to an AI-ready semantic setup that supports diagnostics, predictive maintenance and strategic decisions.

You stay hands on enough to build and debug models when needed, but most of your time goes to standards, cross-domain conflicts and final technical decisions on the ontology.

Responsibilities

• Own the ontology and knowledge graph strategy for services, engineering and commercial, so domain models work together as one semantic layer.
• Set the modeling standards and patterns every domain model must follow, built on OWL 2, RDF/RDFS, SHACL and SPARQL.
• Settle conflicts and overlaps between domain models and make the final technical call.
• Design how the organization staffs, trains and deploys contractors, Forward Deployed Engineers (FDEs) and subject matter experts (SMEs) across the program.
• Build and improve the training curriculum so SMEs can build and maintain their own domain models.
• Grow the organization's ontology capability so the program scales without depending on any single person.
• Define the model approval framework, including review criteria, the review board or process, and the escalation path for contested or high-risk models.
• Make final decisions on contested or high-risk approvals and delegate routine approvals within the framework.
• Own governance end to end: versioning policy, model lifecycle, access model, and metadata and lineage management.
• Audit the knowledge graph for drift, inconsistency and falling standards as it grows.
• Work with the GenAI/ML and AI platform teams so the knowledge graph grounds RAG and agentic workflows across the program.
• Explain modeling trade-offs and delivery progress clearly to technical teams and senior stakeholders.
• Write reusable standards and reference material, and mentor others to build semantic modeling skill across the team.
• Own the platform selection decision and the long-term roadmap, and define the governance approach as adoption expands.

Requirements

• A bachelor's degree in computer science, information science, data science, engineering or a related field, or equivalent practical experience.
• 4 to 6 years of professional experience in data modeling, semantic technologies or knowledge engineering, with at least 1 to 2 years working directly on ontologies.
• Experience delivering data models, semantic technology or knowledge engineering work, including ontologies or knowledge graphs.
• Hands-on skill with OWL 2, RDF/RDFS, SHACL and SPARQL.
• Experience with at least one ontology, graph or semantic modeling platform, such as Neo4j, Protégé, Palantir Foundry or Atlan.
• Working knowledge of modern cloud data environments and how to connect a semantic layer to enterprise data products.
• A record of delivering a defined technical scope on your own within a wider architecture and product roadmap.
• Strong communication and stakeholder skills, with the ability to turn unclear requirements into clear models and recommendations.
• Ability to work independently on a defined scope while fitting into a broader technical roadmap.
• Preferred: experience in an industrial, energy or manufacturing setting, with services demand, asset performance or engineering data being a plus.
• Preferred: exposure to GenAI or LLM applications, especially RAG architectures built on structured knowledge.
• Preferred: familiarity with Model Context Protocol (MCP) or similar agent-to-data integration patterns.
• Preferred: experience contributing to platform evaluations or architecture decision documents for executive audiences.
• Preferred: knowledge of data governance, metadata management or MLOps practices, such as MLflow.

Conditions

Only candidates legally authorized to work in the United States will be considered for this role.

Any offer is conditional on passing a drug screen where applicable.

Relocation assistance is not provided.

For U.S.-based roles, the base pay range is $119,200.00 to $198,600.00, and the company applies a geographic differential of 110%, 120% or 130% of salary in certain areas.

Actual pay depends on factors such as experience, education and skill set.

The posting will stay open for at least seven days after its October 08, 2026 posting date.

Benefit plans can be changed or ended at any time at the company's discretion, and this document does not form an employment contract.

The company is an equal opportunity employer.

Benefits

• Discretionary annual bonus.
• Medical, dental, vision and prescription drug coverage.
• Health Coach, a nurse-based resource available 24/7.
• Employee Assistance Program offering confidential assessment, counseling and referral, available 24/7.
• Retirement Savings Plan, a tax-advantaged 401(k) with company matching and company retirement contributions, plus Fidelity resources and financial planning consultants.
• Tuition assistance, adoption assistance, paid parental leave, disability benefits and life insurance.
• 12 paid holidays and permissive time off.