Product Manager, AI Analytics & Workflows

Role Overview

Product Manager for AI Analytics & Workflows who thrives in ambiguous, fast-moving environments. You will translate customer workflows, data realities, and model outputs into clear product requirements that enable high-quality delivery, adoption, and measurable impact.

Responsibilities

• Translate customer workflows and model-driven outputs into PRDs, acceptance criteria, and well-scoped Jira tickets.
• Define analytics requirements so customers can measure success and navigate insights.
• Partner with Engineering and Data Science/ML to clarify tradeoffs related to model behavior, edge cases, and usability.
• Improve product-led onboarding via in-app guidance, documentation, and workflow education to reduce time-to-value.
• Support release readiness by coordinating launch communication, release notes, and internal enablement.
• Ensure user-facing documentation matches the current platform state.

What You’ll Achieve

• First 6 months:
  • Build understanding of Mpathic’s core platform.
  • Ship roadmap items by writing high-quality PRDs and analytics requirements.
  • Improve onboarding and support release coordination.
• First year:
  • Own high-impact product workstreams across AI workflows, analytics requirements, and onboarding.
  • Improve product quality measurement and communicate trust/quality tradeoffs (data quality and model performance).
  • Strengthen traceability and customer-facing reporting for actionable insights.

Requirements

• 3–5 years of experience in Product Management (or closely related role).
• Strong PRD writing skills and ability to turn real customer workflows into specific, testable requirements.
• Comfortable working with AI/ML and data science teams.
• Ability to define analytics requirements: dashboards, metrics, and insight traceability.
• Strong product thinking centered on measurement, iteration, and prioritization using adoption and workflow metrics.
• Ability to use data to inform product decisions.
• Clear communication with technical and non-technical stakeholders, especially around tradeoffs and edge cases.
• Focus on explainable, reliable product experiences that customers can act on.

Nice-to-Haves

• Experience with explainability/trust concepts in AI products and working knowledge of clinical/research workflow contexts (implied by cross-functional stakeholders).