AI & Data Semantics Lead

About the Role: AI & Data Semantics Lead

Outcome Logix LLC, a direct client of Dice, is seeking an experienced AI & Data Semantics Lead to play a pivotal role in enabling AI and LLM initiatives within the banking sector. This remote position focuses on defining, documenting, and governing business meaning for data assets, translating complex business concepts into structured semantic artifacts, and ensuring data integrity and ethical AI usage.

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.

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 Skills/Competency

• AI Enablement
• Data Semantics
• Business Analysis
• Metadata Management
• Taxonomy Development
• Data Governance
• Stakeholder Engagement
• LLM Integration
• Data Catalog
• Semantic Modeling