[Hiring] Principal Product Manager @Asurion

Role Description

As a Principal Product Manager, you will join the Intelligence Platform team and own the strategy, development, and performance of our customer-facing AI agents and our Agent Marketplace. You will sit at the intersection of product management and data science, focused on building AI-powered experiences across the full lifecycle care journey while driving the composable platform that makes every AI investment reusable across the organization.

You will partner directly with our Data Science team to advance the capabilities of our AI agents, working alongside Engineering, Design, Care Operations, and business stakeholders. This is a hands-on product role where you will shape how our AI agents interact with customers, help move us toward a more agentic approach where AI can reason, plan, and act on behalf of customers, and drive the marketplace strategy that accelerates delivery of new AI-powered programs from weeks to days.

Key Responsibilities

• AI Agent Strategy & Roadmap:
  • Own the product vision, strategy, and roadmap for customer-facing AI agents across digital (chat, messaging, app) and voice (IVR, voice assistant) channels.
  • Proactively shape the roadmap to move from scripted automation toward agentic experiences that can reason, plan, and take action, improving resolution rates and reducing customer effort.
  • Define and track success metrics that tie AI agent performance to measurable outcomes: resolution rate, customer satisfaction, containment, handle time reduction, and self-service coverage.
  • Evaluate emerging AI capabilities (large language models, multi-modal AI, tool use, memory, retrieval-augmented generation) and make clear recommendations on what to adopt and when.
  • Understand the pulse of the business and anticipate what our AI agents need to do next, not just what they do today.
• Data Science Partnership:
  • Serve as the primary product partner to the Data Science team, co-developing the AI capabilities that power our customer-facing agents.
  • Translate business needs and customer pain points into clear problem statements that data scientists can build against, with well-defined success criteria.
  • Engage in technical trade-off conversations around model architectures, training approaches, prompt engineering, and evaluation methods.
  • Partner on experimentation strategy, A/B testing frameworks, and model performance benchmarking to continuously improve agent quality and expand coverage.
• Lifecycle Care Journey:
  • Map AI agent capabilities across the full customer lifecycle (onboarding, troubleshooting, claims, upgrades, retention) and prioritize the opportunities with the greatest customer and business impact.
  • Drive a cohesive cross-channel experience so customers get consistent, intelligent support whether they engage via chat, voice, or self-service.
  • Collaborate with Care, Operations, and CX teams to understand frontline realities and make sure AI agents complement human experts rather than creating friction.
• Cross-Functional Execution:
  • Work across Engineering, Design, Data Science, Care Operations, and business stakeholders to ship AI-powered experiences at pace.
  • Influence leadership on where to invest in AI capabilities and how to sequence bets for maximum return across the Intelligence Platform.
  • Drive alignment between business objectives and technical feasibility, managing trade-offs with transparency.
  • Champion a culture of rapid experimentation, learning from failures, and iterating quickly.
• Agent Marketplace:
  • Own the product strategy and roadmap for the Agent Marketplace, a composable platform of production-ready AI skills that enables teams to discover, reuse, and assemble capabilities across domains.
  • Drive adoption across engineering teams by making reuse the default path, tracking metrics like reuse rate, discovery time, developer adoption, and time-to-delivery for new client programs.
  • Partner with domain teams (Claims, Tech Support, Fulfillment, Payments, Customer360, Communications, Channels, Fraud) to grow the catalog and ensure skill quality, documentation, and composability standards.
  • Shape the marketplace roadmap to support the broader platform vision, including semantic search, usage analytics, quality scoring, and future client-facing capabilities.

Qualifications

• 7+ years of product management experience, with meaningful time spent on AI/ML-powered products.
• Hands-on AI experience is a must. You are actively working with AI in your current role (building AI-powered products, partnering with data science teams, shaping AI strategy) or you can demonstrate significant daily use outside of work: vibe coding, building with Claude Code, shipping projects with LLM APIs, building custom GPTs, fine-tuning models, or contributing to open source AI projects.
• Strong understanding of how AI works: large language models, NLP, conversational AI, model training and evaluation, prompt engineering, and agent frameworks.
• Comfortable working with data. You can write basic Python or SQL for analysis, pull insights from model performance dashboards, and use data to drive product decisions.
• Track record of shipping customer-facing products that operate at scale across digital or voice channels.
• Demonstrated ability to partner deeply with data science and engineering teams as a true collaborator, not just a requirements writer.
• Strong communication skills that work in both directions. You can translate complex AI concepts for business stakeholders and articulate business context for technical teams.

Preferred Qualifications

• Experience building or managing conversational AI, virtual assistants, chatbots, or voice AI products.
• Familiarity with agentic AI patterns: tool use, multi-step reasoning, memory and context management, retrieval-augmented generation (RAG).
• Experience in industries with complex care journeys (insurance, telecom, healthcare, fintech).
• Background working with contact center technology, IVR systems, or digital care platforms.
• Direct experience calling LLM APIs (OpenAI, Anthropic, open source models) or working within agent orchestration frameworks (LangChain, LlamaIndex, or similar).
• Experience with platform or marketplace products where reuse, composability, and developer adoption are key success metrics.

Core Competencies

• AI/ML Technical Fluency and Hands-On Experience
• Customer-Facing Product Strategy
• Data Science Partnership and Collaboration
• Cross-Channel (Digital + Voice) Product Thinking
• Agentic AI Vision and Roadmap Execution
• Platform and Marketplace Strategy
• Analytical Rigor and Experimentation Mindset
• Cross-Functional Influence and Stakeholder Management