Product Manager

Description;

• Project Background:
o  AI-driven Claims Copilot product aimed at improving claims management processes.
o Initial pilots (e.g., Fraud Detection, Document Translation, and Summarization) were solution-led experiments rather than user-centered discoveries.
• Purpose of the Roles:
o Address gaps in early-stage product discovery.
o Ensure a structured, user-centered approach to identifying and implementing AI solutions.
o Accelerate the delivery by providing clear, actionable product directions for future internal teams.
The PM contractor will focus on leading early-stage product discovery and structuring the roadmap for the project. Key requirements include:
• Experience:
o Leading discovery sessions and creating feature roadmaps.
o Product management expertise, preferably with AI-related projects.
o Business casing and assessing the value and viability of AI features/solutions.
• Mandate Objectives:
o Frame core problem areas in claims where AI can deliver meaningful and safe value.
o Assess technical feasibility and identify gaps in collaboration with the AI/Data Science team.
o Structure a roadmap and business case for scaling or pivoting the product.
o Leave behind clear, documented product directions for internal teams to use in 2026.
• Collaboration Model:
o Work closely with:
Internal Data Product Management team to frame opportunities, validate user needs, and build the roadmap.
AI/Data Science team to understand technical feasibility.
Claims management team and other SMEs to validate use cases and map workflows.
o Proactively lead workshops, interviews, and discovery artifacts while maintaining transparency and sharing learnings with the internal team.
o Deliver concise, decision-enabling outputs rather than lengthy reports

MUST HAVE;
• Experience in leading generative AI-related product discovery, ideally in insurance or banking settings.
• Ability to translate user workflows and pain points into use cases and features, even when users struggle to articulate them.
• Strong technical fluency in:
o LLMs (Large Language Models),
o Prompt engineering,
o Data pipeline needs and Data architecture. Note: not a Data Scientist but someone that can speak/understand the language used
• Capability to assess feasibility, risk, and business impact, and to shape a strategic roadmap linked to outcomes (not just a backlog).
• Excellent documentation and communication skills to ensure alignment and knowledge transfer.
• Familiarity with compliance, privacy, and risk concerns in AI and regulated environments.

Nice Have;
Microsoft Copilot

Physical Presence: Hybrid – 3 days per week in office