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
AI-native workflow. You use Claude, ChatGPT, Cursor daily as your analytical operating system. You prototype by prompting before you code. You generate SQL, debug logic, draft executive summaries, and pressure-test your own models with AI. You have opinions on which tools are better for which tasks.
5–10 years in GTM analytics, strategy consulting, or revenue analytics at a high-growth B2B SaaS company. You know which metrics matter at each stage from $10M→$100M.
Management consulting foundation (MBB or equivalent).
Intellectual curiosity about how AI-native products change measurement. You're not satisfied applying last generation's engagement metrics to a product where AI agents do the heavy lifting.
Product analytics depth. You've worked with product telemetry data: activation funnels, feature adoption, retention cohorts — and connected it to revenue outcomes. You partner with Product and Data Engineering to define the instrumentation that matters, not just consume what's already tracked.
Deep SaaS fluency: ARR, NDR, pipeline velocity, cohort LTV, CAC payback. You think in unit economics and systems, not charts.
Strong SQL. Production queries against BigQuery or Snowflake, dbt models, dashboards in Looker or equivalent. You're hands-on, and you don't need a data engineer to unblock your path to output.
Quantitative modeling: forecasting, account scoring, predictive churn, scenario analysis. You've built models that influenced real resource allocation decisions, not just slide decks.
End-to-end pipeline analysis: lead to close to renewal to expansion. You identify bottlenecks, quantify leakage, and deliver recommendations that change behavior.
Board-level communication. You present complex analysis to the CEO and board in clear, actionable terms. You know the difference between a data readout and a strategic recommendation.
You ship fast. AI copilots mean you operate at 3x traditional output and invest the time saved in deeper thinking and higher quality insights, not more dashboards.
STEM or BS in Finance, Economics
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.
- We make GTM decisions on data — surfaced faster because AI does the heavy lifting and you do the thinking.
