Head of GTM Intelligence & Analytics

About Us:

Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.

The Role

Reporting to the Head of Sales Strategy & Analytics, the Head of GTM Intelligence & Analytics owns how Fireworks sees its go-to-market. You define the metrics, build the dashboards, and lead the analyses that sales, finance and executive leaders rely on in QBRs, forecast calls, and Pipeline Council.

You will lead a small team of two in the near term, work closely with Finance, Product and Data Engineering so that revenue, pipeline and product usage numbers are trusted and defined consistently, and personally lead the hardest analytical problems, such as churn analysis and propensity models. As Fireworks grows from a handful of sellers to a global field organization, the questions get harder and the decisions get bigger. This role makes sure leaders have the right answer, quickly.

This is a player-coach seat. You are expected to build and review the work yourself while developing the people on your team. Consumption and hybrid commit + usage economics are core to how we sell and how our business grows, so the right candidate has hands-on experience analyzing usage-based or hybrid commercial models, not only license ARR.

Responsibilities

Metrics, Definitions & Dashboards

Own the definitions for Fireworks' critical GTM metrics (for example pipeline stages and conversion, bookings, ARR, consumption, churn, and net revenue retention), agree them with Finance, RevOps and the Product team (for example how product usage, adoption and consumption are defined), and keep them documented and versioned.

Build, or direct the team to build, the dashboards leaders use every week, with one agreed source for each number.

Retire conflicting reports and make it clear which numbers are official.

Advanced Analytics & Modeling

Lead complex analyses end to end, including churn and retention analysis, propensity models (conversion, expansion, churn), cohort and usage-to-revenue analysis (working with Product on usage signals), and pipeline conversion and win-rate analysis.

Turn analysis into clear recommendations for sales, finance and executive leaders, and track whether they worked.

Establish standards for model validation, so leaders can trust and reuse results.

Operating Cadence Readiness

Make sure the data and tooling behind QBRs, weekly forecast calls, and Pipeline Council are accurate, timely and repeatable.

Track forecast accuracy and pipeline coverage, and explain misses.

Reduce manual preparation by automating recurring packs and reports.

Partnering with Finance, Product & Data Engineering

Work with Finance to reconcile bookings, ARR and consumption to the financial plan and reported results.

Work with the Product team to define product usage and adoption metrics (for example active usage, model and feature adoption, and the drivers of consumption), and connect them to pipeline, expansion and churn analysis.

Partner with Data Engineering on source-of-truth data models across CRM, billing/usage and roster/quotas, so calculations are reproducible and auditable.

Define data-quality checks and priorities, and escalate gaps with clear requirements.