Artificial Intelligence

• Transform business requirements into conceptual, logical and physical data models for AI initiatives and the Abacus data model and metadata.
• Use Databricks Unity Catalog to build a semantic layer, manage AI assets and registered metric definitions.
• Leverage AI Assistant and SQL Agents to build and test the semantic layer.
• Build conceptual, logical and physical data models aligned with enterprise architecture, business requirements and industry standards.
• Follow data modeling best practices, including schema evolution, lossless data modeling, metadata management, and data versioning.
• Use enterprise data modeling tools (Erwin, ER/Studio, DBT, or similar).
• Ensure models support analytical (data science, actuarial, reporting) and operational (Claims, Care Management) use cases.
• Maintain metadata, business glossary, entity relationship diagrams and data dictionaries to support data lineage and governance.
• Partner with Client Management, Product, and Business Analyst teams to understand client needs, assess data model impact, and support client implementation.
• Perform data profiling analysis.
• Use SQL, data modeling and data integrity principles.
• Collaborate with Director of Data Modeling and Senior Data Modelers to adhere to data modeling standards, metadata governance, and data architecture strategies.

AI Data Modeler

Patent Engineer – AI/ML, Kernel & High-Performance Compute

Patent Engineer – AI/ML, Kernel & High-Performance Compute

AI Catalyst

Senior Engineer, AI & ML

Senior AI Analyst