AI Product Manager

AccordionBoston, MA
2dHybrid

About The Position

We’re looking for an AI Product Manager to join one of Accordion’s new AI-augmented delivery pods—purpose-built teams that combine AI engineering, data science, and product management to transform how PE-backed companies run finance and operations. In this role, you are the translation layer between client problems and AI system design. You don’t manage Jira tickets, you shape what gets built and why. You’ll run discovery sessions, write tight requirements, and keep sprints moving at a pace that only works if you’re using AI to think faster, write better, and communicate more clearly. We move at sprint pace, not quarter pace. If you want to own outcomes end-to-end, work directly with PE clients, and build things that matter in finance, this is the role.

Requirements

  • AI tools are woven into how you work daily, you are materially faster because of it, and you can demonstrate that concretely
  • 3–6+ years of PM or delivery management experience in a technical or AI product context
  • PE or finance domain experience: FP&A, financial close, ERP systems, or finance transformation programs
  • Background in consulting or professional services delivery
  • Experience with the commercial dimension of AI products—thinking about adoption, product-market fit, and client value capture, not just delivery
  • Demonstrated ability to translate messy, ambiguous business problems into clear engineering requirements
  • Experience working directly with engineers on AI/ML or data-intensive products
  • Strong written communication skills, you can write a tight PRD, user story, or functional spec (and you can do it fast)
  • Comfort with iterative, sprint-based delivery under real time pressure. The world is changing around us, and you’ll drive our products as an agent of change.

Responsibilities

  • Interface with executive clients, garner trust in our services, and fill in your own knowledge gaps in real time.
  • Translate ambiguous client problems into clear engineering requirements: PRDs, user stories and functional specs (and do it fast)
  • Run client discovery sessions and earn trust quickly, including with CFOs and PE operators
  • Own sprint planning and delivery coordination across your pod’s AI engineers and data scientists
  • Define success criteria and KPIs for AI-powered features before they are built, not after
  • Identify and communicate when an AI system isn’t performing reliably; bridge the gap between technical diagnosis and client-facing communication
  • Use AI tools for 50%+ of your written output: requirements, stakeholder communications, research, and sprint planning
  • Travel to client site as needed
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