Staff People Analyst - Applied AI

About the role

The People & Places Analytics, Research, and Technology (PART) team is looking for a Staff Business Data Analyst to drive applied AI experimentation across Talent Analytics, workforce planning, and broader people insights. AI is reshaping how People Analytics can operate, but turning that opportunity into deployed value requires someone who builds and tests AI solutions directly, not just commissions them. This role will rapidly prototype AI proof-of-concepts against people data, stress-test which ideas are worth scaling, and serve as the team's connective tissue to both Intuit's enterprise AI Transformation team and the enterprise AI contextual layer, ensuring local experimentation compounds into enterprise capability instead of duplicating it. This is a hands-on builder role for someone comfortable operating in ambiguity, translating between technical experimentation and business need, and representing the team credibly in cross-org AI initiatives.

Responsibilities

• Design, build, and iterate rapid AI proof-of-concepts and experiments across the team's domain (talent analytics, workforce planning, talent acquisition, and related people-data use cases), using modern AI/LLM tooling to validate ideas before committing engineering investment.
• Design agent-assisted analytics for these pilots with explicit decision boundaries — specifying what an agent may decide or act on autonomously versus what must escalate to a human, grounded in governed data.
• Continuously scan for new AI capabilities, tools, and use cases applicable to talent analytics and workforce planning, maintaining a point of view on what's worth piloting next.
• Define the dashboard and visualization patterns for prototypes that graduate toward self-serve or conversational analytics, and identify which underlying data sources are certified for that use.
• Label AI-generated outputs and prototypes by confidence tier (e.g., exploratory, validated, decision-ready) so stakeholders know how much weight an insight can bear, and ensure provenance is traceable.
• Partner with the AI Transformation team to align local experimentation with enterprise AI standards, tooling, and roadmap, so pilots aren't reinvented or left orphaned.
• Bring enterprise capabilities into the team's use cases and feed learnings back to the enterprise team.
• Work with Technology and HR organization to contribute to and consume the HR contextual layer, encoding the team's domain definitions and business logic so both people and agents can rely on them consistently as shared context infrastructure matures.
• Provide reliable estimates and establish a shared, spec-writing discipline for ambiguous, large-scope initiatives so that AI-assisted work is planned with the same rigor as any other delivery.
• Embed AI into the team's analytics product roadmap