Staff AI Analytics Lead — Applied Talent Insights

Intuit; San Diego (CA); On-site; USD 151,000 - 204,000; Full time

Job summary

Intuit is seeking a Staff Business Data Analyst to prototype AI solutions within Talent Analytics and workforce planning, translating technical experiments into business value. The role collaborates with enterprise AI Transformation and contributes to the HR contextual layer, ensuring scalable, governed AI capabilities across the organization.

The candidate will operate in ambiguity, designing rapid PoCs, dashboards, and governance for AI outputs, while delivering with cross-org stakeholders and

Qualifications

• Requires hands-on ownership of building and shipping AI solutions.
• Experience building and deploying AI agents or LLM-powered tools.
• Knowledge of data warehouse, data pipelines, and data architecture for AI analytics readiness.
• Experience with dashboard/BI design and self-serve analytics; ability to certify data sources.

Responsibilities

• Prototype AI proofs-of-concepts and experiments across talent analytics and workforce planning.
• Design agent-assisted analytics with explicit decision boundaries and governance.
• Develop self-serve dashboards and visualization patterns for prototypes.
• Partner with enterprise AI Transformation and contextual layer teams to align standards.
• Plan production handoffs with data engineering and product, with clear evaluation criteria.

Skills

AI experimentation

LLM tooling

Data analytics

Dashboard design

Stakeholder communication

Education

Bachelor's degree in AI/Data Analytics/CS

Tools

Claude / GPT

Workday HCM

ATS platforms (Avature)

Job description

Intuit is seeking a Staff Business Data Analyst to prototype AI solutions within Talent Analytics and workforce planning, translating technical experiments into business value. The role collaborates with enterprise AI Transformation and contributes to the HR contextual layer, ensuring scalable, governed AI capabilities across the organization.

The candidate will operate in ambiguity, designing rapid PoCs, dashboards, and governance for AI outputs, while delivering with cross-org stakeholders and