GTM & AI Analytics Director

Ivo•San Francisco, CA

2d•Onsite

About The Position

Ivo is seeking a GTM & AI Analytics Director to measure how AI products deliver value to customers and utilize AI tools for faster and deeper analysis. This role is not traditional, as it involves analyzing AI-native products that interact with customers in novel ways, such as autonomous contract review and LLM-powered intelligence queries. The ideal candidate will invent new frameworks for measuring business impact when AI agents perform tasks autonomously. Reporting to the VP Revenue Strategy & Operations, this position will own GTM analytics end-to-end, including pipeline health, forecast modeling, win/loss analysis, rep productivity, expansion propensity, churn risk, and product usage metrics. The role involves partnering with GTM Operations and GTM Systems & Automation teams, and translating data into action. As pricing models evolve, the director will build consumption analytics, linking product telemetry to revenue, activation, retention, and expansion triggers, and quantifying the ROI of strategic initiatives.

Requirements

AI-native workflow: daily use of Claude, ChatGPT, Cursor as an analytical operating system.
Prototyping by prompting before coding.
Generating SQL, debugging logic, drafting executive summaries, and pressure-testing models with AI.
5–10 years in GTM analytics, strategy consulting, or revenue analytics at a high-growth B2B SaaS company.
Understanding of key metrics from $10M to $100M ARR.
Management consulting foundation (MBB or equivalent).
Intellectual curiosity about how AI-native products change measurement.
Product analytics depth: experience with product telemetry data (activation funnels, feature adoption, retention cohorts) and connecting it to revenue outcomes.
Partnership with Product and Data Engineering to define essential instrumentation.
Deep SaaS fluency: ARR, NDR, pipeline velocity, cohort LTV, CAC payback.
Strong SQL skills: production queries against BigQuery or Snowflake, dbt models, dashboards in Looker or equivalent.
Hands-on approach, ability to work independently without a data engineer for basic tasks.
Quantitative modeling: forecasting, account scoring, predictive churn, scenario analysis.
Experience building models that influenced real resource allocation decisions.
End-to-end pipeline analysis: lead to close to renewal to expansion.
Board-level communication skills: presenting complex analysis to CEO and board clearly and actionably.
STEM or BS in Finance, Economics.
Relentlessly resourceful with a strong internal sense of urgency.
Bias towards action and doing things today.
Experience working in a startup environment is preferred but not required.

Nice To Haves

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 an analytical field.

Responsibilities

Measure how AI products deliver value to customers.
Utilize AI tools to perform analysis faster and deeper than traditional methods.
Diagnose pipeline bottlenecks, segment performance, and identify rep-level issues.
Build propensity models, identify market white space, and provide prioritized target lists.
Invent new frameworks for measuring business impact for AI-native products.
Own GTM analytics end-to-end: pipeline health and velocity, forecast modeling, win/loss analysis, rep productivity, territory performance, expansion propensity, churn risk.
Analyze product usage metrics that connect customer interaction with AI to renewal, expansion, and advocacy.
Partner with GTM Operations, GTM Systems & Automation, and the tech team to translate data into action.
Build consumption analytics as pricing evolves towards usage-based and outcome models.
Link product telemetry to revenue, activation cohorts, retention curves, and expansion triggers.
Quantify the ROI of strategic bets before they are made.
Present complex analysis to the CEO and board in clear, actionable terms.
Operate at 3x traditional output using AI copilots, investing saved time in deeper thinking and higher quality insights.