AI Data Scientist

About the role

The AI Data Scientist role sits at the intersection of data governance, knowledge management and AI delivery. You will own the quality and accessibility of the knowledge libraries that power AI systems, ensuring that models are fed with trustworthy, up‑to‑date information. You will work closely with AI Engineers to connect retrieval pipelines to clean, context‑aware data stores.

Key responsibilities

• Design, implement and maintain data catalogues for AI projects using platforms such as DataHub, OpenMetadata, Apache Atlas, Collibra or cloud‑native equivalents (AWS Glue Data Catalog, Azure Purview, GCP Dataplex).
• Define metadata schemas and taxonomy standards – type, version, jurisdiction, validity period, confidence tier – to enable precise retrieval and trust assessment.
• Assess data quality across client and internal assets with tools like Great Expectations, dbt tests or Soda, flagging stale or ambiguous records before they reach the AI layer.
• Build and maintain data lineage so every AI‑generated output can be traced back to its source, version and validity period.
• Design automated ingestion workflows and nightly quality checks to keep catalogues current without manual effort.
• Partner with engineering teams to integrate RAG pipelines and agentic retrieval systems with catalogue APIs.

Required profile

• Background in data governance, information management, knowledge management or records management.
• Strong interest in how robust data catalogues and metadata enable reliable AI systems.
• Experience working with cross‑functional technical teams.

Required skills

• DataHub, OpenMetadata, Apache Atlas, Collibra (or similar catalogue tools).
• AWS Glue Data Catalog, Azure Purview, GCP Dataplex.
• Great Expectations, dbt, Soda for data quality testing.
• Data lineage design and implementation.
• API integration for retrieval pipelines.