Job Title: AI & Data Semantics Lead (Business-Facing)
Location: Remote
Temp to Hire (Start Date: 04-20-2026 - End date: 09-24-2026)
Visa's: Independent Visa's ONLY
Tax Terms: W2 ONLY
Job Description:
Role Summary
We are seeking an AI & Data Semantics Lead to accelerate our LLM and AI model teams by serving as the critical bridge between business stakeholders, data products, and technical AI teams. This role is responsible for translating complex technical data assets into clear, governed, business-understandable semantics that can be safely and effectively leveraged by AI models. The ideal candidate is exceptionally strong in business analysis, comfortable working in enterprise data catalog environments, and experienced in building taxonomies, business glossaries, and semantic layers that scale across teams and use cases. This role will directly support the success of agentic capabilities, AI use cases, and data product adoption by ensuring models understand what data means, not just where it lives.
Required Qualifications
- 7+ years of experience in business analysis, data analysis, or data product roles
- Demonstrated experience working in a data catalog or metadata management platform (e.g., Alation or equivalent)
- Hands-on experience building:
- Business glossaries
- Taxonomies / classification models
- Semantic layers or conceptual data models
- Strong ability to translate technical data assets into business language
- Proven experience partnering with technical teams (data engineering, analytics, AI/ML)
- Excellent facilitation, documentation, and stakeholder communication skills
Key Responsibilities
LLM & AI Enablement
- Partner with LLM and AI model teams to define, document, and govern business meaning for data assets used in training, inference, and agentic workflows.
- Translate business concepts into structured semantic artifacts (business terms, classifications, relationships) consumable by AI systems.
- Support responsible AI by ensuring data assets have clear definitions, ownership, lineage context, and usage constraints.
Business Analysis & Stakeholder Engagement
- Lead discovery sessions with business stakeholders to extract domain knowledge and convert it into reusable semantic assets.
- Act as a trusted translator between business leaders, data product owners, engineers, and AI practitioners.
- Decompose ambiguous business questions into well-defined data concepts and analytical intent.
Metadata, Catalog & Taxonomy Development
- Build and maintain enterprise business glossaries, taxonomies, and classification frameworks within a data catalog environment.
- Curate and enrich technical assets with business context (descriptions, relationships, use cases, examples).
- Ensure semantic consistency across domains, data products, and AI use cases.
Data Product & Platform Alignment
- Align semantic definitions with data products, certified assets, and governed data sources.
- Partner with data governance, data quality, and lineage teams to ensure metadata completeness and trust.
- Contribute to standards and patterns for AI?ready metadata and semantic modeling.
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