Senior Product Manager, Customer Lifecycle

ServiceTitan ServiceTitan · Enterprise · United States · Remote

Senior Product Manager to own AI-powered use cases within an agentic customer lifecycle experience, focusing on LLMs and customer outcomes for B2B SaaS.

What you'd actually do

  1. Own a portfolio of AI-powered use cases within our agentic customer lifecycle experience. You'll be responsible for specific customer outcomes and ensuring our AI agents have the capabilities to achieve those for our customers.
  2. Drive discovery for how AI can help customers optimize their usage of ST and get the help they need, and how we can drive AI capabilities in new channels for customers.
  3. Collaborate deeply with engineering and data science to shape the context layer and how we can create delightful customer experiences in any/all channels.
  4. Partner with post-sales teams (Support, Customer Success) to uncover customer and team needs. Identify opportunities for scalable, automated approaches vs. manual ones.
  5. Measure impact. Define the right success criteria and ensure we are evaluating and improving accuracy and coverage at all times. Use the data to inform what we do next.

Skills

Required

  • 5+ years of Product Management experience in B2B SaaS
  • at least 2 years shipping AI/ML-powered products in production
  • Experience shipping agentic AI solutions
  • Fluency in eval frameworks, deterministic vs. non-deterministic approaches and where each is appropriate, and data-driven iteration on model performance.
  • Comfort with quantitative data assessing every aspect of the journey from customer behavior to AI agent accuracy to business outcomes.
  • Ability to work effectively with operations leaders, engineering teams, and data scientists.
  • Frame AI capabilities in terms of customer and business outcomes, not just technical sophistication.
  • write clearly, present concisely, and build conviction with cross-functional partners.

Nice to have

  • Experience in support/CX, customer lifecycle, or post-sales product management.

What the JD emphasized

  • shipping AI products in production
  • Experience shipping agentic AI solutions
  • Fluency in eval frameworks
  • data-driven iteration on model performance
  • agent performance data

Other signals

  • AI-powered experiences
  • agentic customer lifecycle
  • LLMs
  • shipping AI products in production