Senior Product Manager, AI Analytics

About the Team

The AI & Analytics pillar powers how Klaviyo's customers understand their business, trust their data, and take action on insights. Our work spans the full analytics stack: from the foundational reporting infrastructure that every team at Klaviyo builds on top of, to AI-powered experiences that help marketers understand performance, explore their data, and take action — all from within the Klaviyo platform.

As Klaviyo expands into Enterprise, Omnichannel, and AI-driven marketing, our analytics platform is evolving to match. That means modernizing the infrastructure underneath reporting, building AI experiences that are genuinely trustworthy and differentiated, and extending analytics to serve the needs of multi-brand Enterprise customers for the first time.

About the Role

We're looking for a Senior Product Manager to own Klaviyo's Analytics AI product area — a cluster of tightly connected product challenges at the intersection of AI, data infrastructure, and analytics UX.

Your primary and largest ownership area is the Analytics Agent: Klaviyo's in-product AI inside Composer that answers performance questions, surfaces insights, and serves as the shared intelligence foundation that every other Expert Agent in Klaviyo relies on for data grounding.

How do you build an AI that gives answers a marketer can trust — ones that match the numbers already in the app? How do you go beyond returning metrics to delivering the "why" and the recommended next action? How do you reach data that generic LLMs over public APIs structurally cannot, and translate complex query infrastructure into simple, intuitive experiences that customers actually use? These are the questions this role is organized around.

Beyond the Analytics Agent, this PM will own AI-native analytics UX (data visualization skills, AI-generated dashboards), the PM-side of Klaviyo's query infrastructure modernization, multi-brand organization analytics experiences, and ongoing platform health for core reporting surfaces.

This is a high-visibility, high-leverage seat. The work you deliver will be felt across Klaviyo's entire agentic product suite, the Analytics SKU, and the Enterprise customer base.

How You'll Make a Difference

• Own strategy and H2+ roadmap execution for the Analytics Agent across five pillars: Trust (quality, correctness, and reaching parity), Intelligence (metrics → insight → recommendation → action), Breadth (expanding to data that public APIs can't reach), Foundation (agent-as-a-tool architecture for Composer's multi-agent orchestration layer), and Experience (AI-generated charts, dashboards, and proactive in-context insights)
• Build and own the quality measurement system for the Analytics Agent — design evals, LLM-as-judge scoring, and a golden set of production cases so the team can measure quality improvements reliably every week
• Own AI-generated analytics UX: define the right integration model between Composer's charting infrastructure, Core Reporting, and Analytics AI; ship a coherent NLI-driven visualization and dashboard creation experience
• Own the PM-side of Klaviyo's query infrastructure transition — as query execution engine migrates and new analytics capabilities become possible, define what that unlocks for customers, sequence migration priorities by customer value, and ensure the transition doesn't create gaps in existing surfaces
• Partner closely with the hiring manager on multi-account analytics & activation experiences for Enterprise Organizations
• Work cross-functionally to drive multi-team alignment across a fast-moving, architecturally complex product area
• Write clear, rigorous product specs for AI behaviors, data-heavy features, and platform-level changes — artifacts that reflect deep understanding of edge cases, data semantics, and technical tradeoffs
• Maintain accountability for the health, quality, and steady-state reliability of core reporting surfaces

Who You Are

• You have 3–5+ years of product management experience, with a strong track record of independently owning complex, multi-stakeholder product areas from strategy through execution
• You have direct, hands-on experience building AI or ML-powered products — not just products that use AI as a feature, but products where model quality, evaluation, and data grounding were core PM responsibilities
• You have a strong data and analytics product background — you've built or owned analytics, reporting, data visualization, or BI products and you care deeply about data accuracy, metric consistency, and customer trust in data
• You have demonstrated UX judgment for complex data experiences: you translate hard query infrastructure problems into simple, intuitive user-facing patterns, and you have a strong opinion on what makes a great data product
• You're comfortable working alongside major platform migrations or infrastructure transitions and can translate technical architecture shifts into product decisions and customer-value sequencing
• You can influence multi-team roadmaps without direct authority — you've navigated competing priorities across platform, feature, and infrastructure teams and made things move
• You write clear, rigorous product specs for data-heavy and AI features — your artifacts reflect deep understanding of edge cases, failure modes, and technical tradeoffs
• You're technically fluent enough to reason about LLM-based systems (evals, agent architecture, tool-use, retrieval), query infrastructure (semantic layers, schema design, query performance), and how they connect to customer-facing product decisions

Nice to Have

• Prior ownership of an in-product AI assistant, chatbot, or analytics agent in a B2B SaaS context
• Experience designing agent-to-agent interfaces or building products within a multi-agent orchestration system
• Familiarity with semantic data layers, query abstraction, or analytics infrastructure modernization projects
• Background in e-commerce, martech, or CDPs/CRMs, or marketing analytics — you understand how marketers think about campaign performance, attribution, and audience analysis
• Experience working closely with data engineering or data platform teams as a PM partner
• Experience supporting Enterprise or upmarket customers with complex, multi-account or multi-brand data needs