Head of GTM Intelligence & Analytics
About The Position
The Head of GTM Intelligence & Analytics owns how Fireworks measures and understands its go-to-market performance. Reporting to the Head of Sales Strategy & Analytics, this player-coach defines critical metrics, builds dashboards, and leads analyses used by sales, finance, and executive leaders in QBRs, forecast calls, and Pipeline Council. The role leads a near-term team of two and works closely with Finance, Product, and Data Engineering to ensure revenue, pipeline, and product-usage measures are trusted and consistently defined. It also personally leads complex analytical work, including churn analysis and propensity models, as the company scales toward a global field organization. Hands-on experience analyzing usage-based or hybrid commit-plus-usage commercial models is important to the role. Fireworks is a platform for specialized intelligence that enables companies to build, train, and serve AI models tailored to their data, workflows, and products. Founded by the team behind PyTorch and backed by several prominent investors, Fireworks offers state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. It is a Series D company valued at $17.5 billion.
Requirements
- At least 8 years of experience in analytics, including at least 4 years in GTM, sales, or revenue analytics.
- Experience leading or coaching analysts and willingness to remain hands-on.
- Advanced SQL and BI/dashboarding skills.
- Experience with statistical or predictive modeling, such as churn or propensity models.
- Experience analyzing a usage-based or hybrid commit-plus-usage business.
- Track record of defining metrics with Finance and Product and working with Data Engineering on source-of-truth data.
- Experience supporting QBRs, forecast calls, board reporting, or executive reporting.
- Clear communication skills, including explaining complex analysis to executives and challenging assumptions with data.
Nice To Haves
- BA/BS degree in engineering, statistics, finance, economics, or another quantitative field.
- Experience at an AI/ML infrastructure, developer tools, or API-first company.
- Experience working through a period of hypergrowth.
- Experience building a metrics or semantic layer, or using modern analytics-engineering tooling.
- Hands-on experience building analytical workflows with AI or agentic tools.
- Familiarity with Salesforce, billing, and product-usage data.
Responsibilities
- Own and document definitions for critical go-to-market metrics, including pipeline stages and conversion, bookings, ARR, consumption, churn, and net revenue retention; align definitions with Finance, RevOps, and Product.
- Build or direct the building of dashboards used by leaders, establish an agreed source for each metric, and retire conflicting reports.
- Lead end-to-end analyses, including churn and retention, propensity models for conversion, expansion and churn, cohort and usage-to-revenue analysis, and pipeline conversion and win-rate analysis.
- Translate analyses into clear recommendations for sales, finance, and executive leaders, and track their impact.
- Establish model-validation standards so leaders can trust and reuse analytical results.
- Ensure data and tooling for QBRs, weekly forecast calls, and Pipeline Council are accurate, timely, and repeatable.
- Track forecast accuracy and pipeline coverage, explain misses, and automate recurring reporting and preparation.
- Partner with Finance to reconcile bookings, ARR, and consumption with the financial plan and reported results.
- Work with Product to define product-usage and adoption metrics and connect them to pipeline, expansion, and churn analysis.
- Partner with Data Engineering to establish source-of-truth data models across CRM, billing and usage, and roster and quota data, ensuring calculations are reproducible and auditable.
- Define data-quality checks and priorities, and escalate gaps with clear requirements.
- Manage, coach, and develop a team of two analytics managers or analysts, and hire as the team grows.
- Set standards for analytical quality, documentation, and speed.
- Use AI and agentic tooling in everyday analysis and demonstrate its use to the team.
Benefits
- Opportunity to solve challenging AI infrastructure problems, including low-latency inference and scalable model serving.
- Opportunity to work with advanced technology affecting how businesses and developers use AI globally.
- Ownership and impact at a fast-growing company, with work that directly shapes its future.
- Collaboration with engineers and AI researchers.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
