Head of Sales Data Science & Analytics

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:

A dynamic leader who inspires and develops teams while maintaining a “roll up your sleeves” attitude — able to step into the details when needed 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 the assumptions required to make credible causal claims from observational data, and able to communicate those tradeoffs clearly to non-technical stakeholders.

Revenue data systems expertise

Gusto is looking for an experienced Senior Data Science Leader to empower our Sales Data team, the team that powers the insights, forecasting, and measurement infrastructure behind how we acquire, expand, and retain our revenue base.

We expect this leader to be both a strategic partner and execution-focused.

You'll guide senior sales and marketing leaders through complex analytical questions, develop analysts into statistical and scientific thinkers, and collaborate cross-functionally with stakeholders across Sales Operations, Data Engineering, Finance, R&D, and more.

As AI tooling becomes deeply embedded in Gusto's data infrastructure, the nature of analytics work is fundamentally shifting.  Tasks that once consumed a significant portion of our analyst's time, such as reporting, dashboard maintenance, and answering ad hoc questions, are increasingly handled by a maturing self-serve ecosystem.

What remains are the hard problems: ones that require rigorous statistical thinking, causal reasoning, and the ability to draw defensible conclusions in messy, real-world conditions where controlled experiments aren't always possible.

Gusto's sales data foundation has significant technical debt, and this leader will need to partner closely with Data Engineering to assess the current state, architect a modern and scalable data layer, and execute a phased remediation.