Head of Revenue Analytics

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.