Among computer and information research scientists, AI is already used for a large share of the tasks. Measured exposure for computer and information research scientists: 34% (high).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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Intuit has filed one layoff notice in the last twelve months, covering 162 workers, most recently May 2026. These are public filings with state labor departments, and an open role can sit alongside them. See the filings

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About the job

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 AI Agents and Proof-of-Concept Development & Experimentation 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 Self-Serve Analytics, Visualization & Output Trust 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 Enterprise AI Partnership & Standards Alignment 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 Contextual & Semantic Layer Contribution 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 Planning, Roadmap Integration & Production Handoff 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, identifying where AI changes what a product can do rather than bolting it onto existing outputs, and sequencing prototypes so validated ideas have a defined path forward Define what moves from prototype to production, working with data engineering and product to hand off validated solutions with documentation, authored evaluation criteria, and the context needed to sustain them, closing the loop by routing outcomes back into future prototyping Technical Feasibility, Risk & Executive Communication Evaluate what's technically feasible versus speculative, and translate that into clear, actionable recommendations for leadership Communicate technical feasibility, risk, and recommended next steps for AI initiatives to senior stakeholders Capability Building Raise applied-AI capability across the team and partner groups by establishing reusable patterns, tooling standards, and working practices so AI-enabled delivery becomes the team's default rather than one person's specialty Coach senior analysts and partner teams to run AI-assisted projects and agents independently, building their judgment rather than just delivering outputs for them Qualifications Bachelor's degree in AI, Data Analytics, Computer Science, Business, Human Resources, Industrial/Organizational Psychology, or a related field 7+ years of experience in business/data analytics or applied AI/ML roles, with demonstrated hands-on ownership (not just oversight) of building and shipping solutions Direct, hands-on experience building and deploying AI agents or LLM-powered tools/prototypes (e.g., using Claude, GPT, or similar) — this is a builder role, not just a commissioner role Working knowledge of data warehouse, data pipeline, and data architecture concepts, and how they affect AI/analytics readiness at scale Experience with dashboard/BI design and self-serve or conversational analytics patterns, including how to certify data sources for broader consumption Comfort defining confidence tiers or similar governance for AI-generated outputs, and reliably estimating and scoping ambiguous, large initiatives Strong point of view on evaluating technical feasibility vs. speculative AI use cases, with the judgment to make build/no-build recommendations Excellent communication skills, with a track record of translating technical experimentation into decisions for senior stakeholders and executive audiences Ability to operate autonomously in ambiguous, undefined problem spaces — this role sits ahead of established enterprise AI patterns Preferred: Experience with Workday HCM data and Avature or other ATS/recruiting platforms Experience partnering with an enterprise AI/AI Transformation team, platform team, or ML engineering org on shared standards or infrastructure Familiarity with talent analytics or workforce planning domains (headcount, requisitions, planned exits, hiring projections) Familiarity with People Data governance, security, and access considerations Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: Mountain View $169,500 - $229,000 New York $159,500- $215,500 San Diego, CA $151,000- $204,000