# Head of Revenue Analytics

**Company:** [Gusto](https://hotfix.jobs/companies/gusto)
**Location:** Denver, CO, San Francisco, CA, New York, NY
**Role:** Data Science
**Salary:** From $218k/yr
**Experience:** 10+ years
**Skills:** SQL, dbt, Snowflake, Salesforce, Causal Inference, difference-in-differences, synthetic control, propensity score matching, Forecasting, AI/ML, Data Science, Analytics
**Posted:** 2026-07-27

> Lead the Sales Analytics team at Gusto to rebuild data foundations, evolve forecasting with causal inference and advanced methods, drive revenue insights, and shift the team toward strategic statistical thinking in an AI-first environment. Requires 10+ years data science experience including 4+ years leading teams, deep Salesforce and SaaS revenue domain expertise, and hands-on technical skills.

## Job Description

## Responsibilities
- Lead the Sales Analytics team, driving vision and championing a team of data scientists and analysts to deliver impact across customer acquisition and product expansion sales teams.
- Conduct deep-dive analyses to identify revenue drivers and anomalies; translate complex data into clear, actionable insights and recommendations for senior leadership and cross-functional partners.
- Rebuild the data foundation alongside Data Engineering and Service Platform partners to evolve foundational data systems for a world-class sales analytics ecosystem.
- Partner with key stakeholders including the Chief Revenue Officer, Sales Operations, Data Engineering, and the Service Platform team to align on a shared data and infrastructure roadmap.
- Evolve sales forecasting methodology to incorporate more rigorous analytics methods (e.g., propensity scoring, better seasonality controls).
- Leverage Gusto's move toward AI-first development to create self-service analytics capabilities for operations partners; stay ahead of emerging AI tools that can drive efficiency and accuracy in revenue analytics.
- Recruit, mentor, and develop a high-performing, proactive analytics team, shifting the culture from reactive query fulfillment toward strategic analysis. Define excellence for analysts in a world where causal thinking and statistical fluency are the new baseline as AI handles routine reporting.
- Act as connective tissue across the data org: identify opportunities where deeper technical solutions (e.g., from Data Engineering, Analytics Engineering, Data Science, or AI/ML Engineering) could accelerate revenue-driving analytics, and proactively bring the right partners into the conversation.

## Requirements
- 10+ years of experience in data science or related fields, with at least 4+ years leading a growing analytics team.
- Leadership with a builder mindset: inspires and develops teams while maintaining a “roll up your sleeves” attitude — able to step into the details to build reports, run analyses, and troubleshoot.
- Strong command of experimental design, causal reasoning, and quasi-experimental methods (e.g., difference-in-differences, synthetic control, regression discontinuity, propensity score matching) for settings where A/B testing isn't possible. Comfortable navigating assumptions for credible causal claims from observational data and communicating tradeoffs to non-technical stakeholders.
- Deep experience working with Salesforce data at scale, including data extraction strategies, CRM-to-warehouse reconciliation, and challenges of treating Salesforce as a source of truth.
- Proven track record of inheriting messy, tech-debt-laden data environments and rebuilding foundations with long-term scalability in mind. Thinks in terms of systems, not patches.
- Strong SQL skills, hands-on experience with dbt and Snowflake, comfort navigating transformation logic across multiple layers (Salesforce, BI, dbt, dashboards), and ability to mentor analysts on best practices.
- Strong understanding of pipeline management, forecasting, quota and attainment tracking, acquisition and expansion motions, and cross-sell/upsell analytics in a multi-product SaaS environment.
- Ability to negotiate priorities and drive shared roadmaps with platform engineering, data engineering, and sales operations teams. Can go toe-to-toe with service platform managers on technical trade-offs.
- Experience leading teams through significant transformation, raising performance expectations, coaching analysts toward more strategic work, and making tough talent decisions when needed.
- Experience presenting infrastructure roadmaps and analytical insights to executive stakeholders, translating complex data system challenges into clear business terms.
- Clear-eyed view of how AI tooling is reshaping the analyst role; understands that as self-serve and automation absorb routine work, team value lives in statistical rigor, causal thinking, and answering questions that can't be solved with a dashboard. Can articulate that vision and recruit, develop, and retain talent accordingly.
- Proven track record of successfully building and leading analytics functions in a high-growth SaaS or technology environment.

## Nice-to-Haves
- AI-forward orientation and experience leveraging AI tools for analytics efficiency.

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