GTM & AI Analytics Director, Ivo

• 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

Desirable

• 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
• 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