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Intuit

Staff Product Manager, Field Service Industry Workflows (Mid-Market)

Intuit

Mountain View, CA · Staff

$173,500 – $255,000

Listed on Intuit’s own careers site. You apply with them directly — we never stand between you and the employer.

What this role is

This staff-level product manager role owns the financial workflows for mid-market field service companies within Intuit's enterprise suite, focusing on transforming manual processes into AI-native systems that automate job costing, dispatch, billing, and cash flow. The position suits experienced product leaders who can deconstruct complex operational workflows, drive AI adoption, and deliver measurable customer outcomes across a matrixed organization.

Our summary, not Intuit’s wording. The full posting is on their site.

Skills this role names

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What they ask for

Required

  • 8+ years product management experience owning complex multi-step business workflows
  • Bachelor's degree in Business, Finance, Engineering, Computer Science or equivalent practical experience
  • Ability to deconstruct operational processes and translate them into scalable software solutions
  • Track record of identifying unmet customer needs and delivering measurable business impact
  • Experience working directly with mid-market businesses or industry verticals
  • Understanding of core financial concepts including job costing, revenue recognition, WIP, payroll, inventory, and cash flow
  • Ability to bridge operational and financial systems into a unified experience
  • Experience driving clarity and alignment across matrixed organizations
  • Strong executive communication skills and ability to influence without authority
  • Comfort balancing long-term industry strategy with near-term delivery

Nice to have

  • Field service industry experience in HVAC, plumbing, electrical, or construction-adjacent services
  • Experience productizing AI/ML capabilities into real customer workflows
  • Ability to design intelligent automation, predictive insights, or autonomous agents for operational problems
  • Comfort working with data science and engineering teams to ship AI-driven features

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