Human Resources Jobs at Intuit with Visa Sponsorship
Human Resources jobs at Intuit span talent acquisition, HR business partnering, total rewards, and people operations across its global workforce. Intuit has a strong track record of sponsoring international HR professionals, making it a realistic target for visa holders pursuing people-focused careers in the technology sector.
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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. 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
Overview
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. 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
See all Human Resources Jobs at Intuit
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Get Access To All JobsTips for Finding Human Resources Jobs at Intuit
Align your HR credentials to U.S. standards
If you hold certifications like SHRM-CP, SHRM-SCP, or PHR, list them prominently. Intuit's HR hiring teams recognize these benchmarks specifically, and they signal readiness for a U.S.-based HR business partner or talent role without additional onboarding.
Target Intuit's people operations and HRBP postings
Intuit structures its HR org around HR business partners embedded in product and engineering teams. Roles tied to those business units are more likely to carry sponsorship approval than generalist postings, so filter your search accordingly.
Confirm your role qualifies as a specialty occupation
USCIS requires H-1B roles to meet the specialty occupation standard, which means a bachelor's degree in a specific field must be a minimum requirement. HR roles at a technology company like Intuit typically satisfy this when tied to workforce planning or organizational development, not general coordination work.
Time your application around Intuit's fiscal hiring cycles
Intuit's fiscal year starts in August, and headcount approvals often drive a surge in HR hiring in Q1 and Q2 of that cycle. Applying between August and December gives your petition more runway before the April H-1B cap registration window.
Prepare your LCA documentation before the offer stage
Intuit's HR sponsors file a Labor Condition Application with the DOL before submitting an H-1B petition. Understanding what the LCA certifies, specifically the prevailing wage for your HR role in the relevant metro area, helps you evaluate offers and flag discrepancies early.
Use Migrate Mate to surface open HR roles at Intuit
Filtered searches by visa type and employer save time when targeting a specific company. Use Migrate Mate to find current Human Resources openings at Intuit that are tagged for sponsorship eligibility before you reach out to recruiters.
Frequently Asked Questions
Does Intuit sponsor H-1B visas for Human Resources roles?
Yes, Intuit sponsors H-1B visas for Human Resources positions, particularly roles tied to HR business partnering, talent acquisition, and people operations. The role must qualify as a specialty occupation under USCIS standards, so positions requiring a bachelor's degree in human resources, organizational psychology, or a related field are the strongest candidates for sponsorship approval.
How do I apply for Human Resources jobs at Intuit?
You can apply directly through Intuit's careers site or browse current openings filtered by visa sponsorship eligibility on Migrate Mate. When applying, tailor your resume to reflect U.S.-style formatting and highlight any experience supporting HR functions within technology or software companies, as Intuit's HR teams operate in close partnership with product and engineering organizations.
Which visa types does Intuit commonly use for Human Resources positions?
Intuit sponsors several visa categories for HR roles, including the H-1B, E-3 visa for Australian nationals, and TN visa for Canadian and Mexican professionals. F-1 OPT and CPT holders are also supported for early-career HR roles, giving recent graduates a path to build experience before transitioning to an employer-sponsored petition. Green Card sponsorship through EB-2 or EB-3 is available for longer-tenured HR professionals.
What qualifications does Intuit expect for sponsored Human Resources roles?
Intuit typically looks for HR candidates with a bachelor's degree in human resources, business administration, industrial-organizational psychology, or a related discipline. For HRBP and senior HR roles, experience supporting large technology or software organizations is a strong differentiator. Professional certifications such as SHRM-CP or PHR are recognized and can strengthen both your candidacy and the specialty occupation argument for USCIS purposes.
How long does the H-1B sponsorship process take when hiring at Intuit?
The H-1B process involves DOL Labor Condition Application certification, followed by USCIS petition filing after the April cap registration lottery. Standard processing runs several months, while premium processing through USCIS can return a decision within 15 business days. If you're on F-1 OPT, the 60-day grace period and any OPT extension timeline should factor into when you initiate conversations with Intuit's HR and legal teams.