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Posted Apr 12, 2026

AI & Data Semantics Lead (Business-Facing) - Remote - Banking - Direct Client - JOBID687

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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
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