What is the job

What does "usage" mean when the product works autonomously? How do we measure value delivered vs. sessions logged? What's the leading indicator of expansion when the user isn't clicking, but its agent is?

AI is both what you analyze and how you analyze. You'll measure how our AI products deliver value to customers, and you'll use AI tools to do that measurement faster and deeper than any traditional analytics team could.

You're the person who tells us why a number moved and what to do about it. Pipeline slowing? You diagnose the stage, segment, and rep-level bottleneck before anyone asks. Expansion stalling? You build the propensity model, identify the white space, and hand the VP of CSM prioritized target list.

This isn't a traditional analytics role. Ivo builds AI products that interact with customers in ways that didn't exist a few years ago: autonomous contract review, LLM-powered intelligence queries, API-driven workflows. The old playbook for measuring engagement (DAU/MAU, feature clicks, time-in-app) doesn't fully apply when an AI agent does the work and the human reviews the output. You'll need to invent new frameworks for measuring business impact when the product thinks, acts, and delivers value without a user sitting in a UI. If that problem excites you, keep reading.

Reporting to the VP Revenue Strategy & Operations, you'll own GTM analytics end-to-end: pipeline health and velocity, forecast modeling, win/loss analysis, rep productivity, territory performance, expansion propensity, churn risk — and the product usage metrics that connect how customers interact with our AI to whether they renew, expand, and advocate. You'll partner with the Director of GTM Operations (who owns execution) and the Director of GTM Systems & Automation (who owns infrastructure), and the tech team, translating data into action.

As pricing evolves toward usage-based and API consumption models, and eventually outcome models, you'll build consumption analytics: product telemetry linked to revenue, activation cohorts, retention curves, and expansion triggers. You quantify the ROI of strategic bets before we make them.

What you bring

Bonus points:

• Built or deployed AI/LLM-powered analytics workflows — anomaly detection, natural language querying, agent-based reporting.
• Defined new engagement or value metrics for AI-native products where traditional product analytics frameworks didn't apply.
• Product analytics tools (Mixpanel, PostHog) linked to revenue outcomes.
• Account scoring or health scoring models operationalized into CRM workflows.
• Usage-based or consumption revenue model experience.
• Side projects, blog, or open-source contributions in AI-augmented analytics.
• MBA or a graduate degree in analytical field

What does success look like

In 90 days: Self-serve dashboards live — Sales, CS, and Marketing answer their own questions. Weekly executive metrics automated. Leadership has pipeline and forecast visibility they trust for the first time. At least one recurring analysis replaced with an AI-automated workflow. Product analytics baseline established — you've defined what "healthy usage" means for an AI-native product and can explain why.

In 12 months:

• GTM planning is analytically rigorous — targets, coverage guides, capacity models, territory design all data-backed and pressure-tested.

• Expansion analytics operational: white space mapped, health scores validated, propensity models driving prioritization.

• Product analytics are a competitive advantage — you've built measurement frameworks that capture value delivery in ways our competitors haven't figured out yet, and those frameworks directly inform pricing, packaging, and expansion strategy.

• You've built quantitative models that directly influenced strategic investments.