Remote AI Lead for Semantic Layer & Data Product

CSAA; Iowa (LA); Remote; USD 168,000 - 224,000; Full time

Benefits offered by this job

Job summary

CSAA Insurance Group is seeking a Sr. Manager, AI Lead - Semantic Layer, remote in the United States. You will define the vision, strategy, and roadmap for enterprise semantic data and data connectivity, guiding a team to turn business concepts into scalable data products.

You will partner with IT and business leaders to align data models, ontologies, and APIs for analytics, automation, and AI use cases, while ensuring governance and measurable outcomes across domains.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Business, Data Science, or a related field.
• 10+ years of product management experience, including data or data platform products.
• 3+ years leading teams, enterprise-wide programs, or large multi-functional programs.
• Owning products end-to-end with strategy, roadmap, delivery, and performance measurement.

Responsibilities

• Own the end-to-end product lifecycle from discovery to delivery and iteration.
• Define product requirements, success metrics, and release plans.
• Lead the semantic data product and establish scalable, governed definitions across domains.
• Collaborate with data engineering, enterprise architecture, and AI/ML teams to translate concepts into scalable implementations.
• Drive adoption and measurable value through data-connected workflows and governance.

Skills

Data modeling

Product management

Leadership

AI/ML familiarity

Enterprise data

Semantic data

Stakeholder management

Education

Bachelor's degree in CS/Engineering/Business/Data Science

Advanced degree preferred

Job description

CSAA Insurance Group is seeking a Sr. Manager, AI Lead - Semantic Layer, remote in the United States. You will define the vision, strategy, and roadmap for enterprise semantic data and data connectivity, guiding a team to turn business concepts into scalable data products.

You will partner with IT and business leaders to align data models, ontologies, and APIs for analytics, automation, and AI use cases, while ensuring governance and measurable outcomes across domains.