Head of Sales Data Science & Analytics

Role overview

A senior data science leader is needed to head a sales analytics organization that powers the insights, forecasting, and measurement infrastructure behind how a high-growth SaaS business acquires, expands, and retains its revenue base. The role carries two equal mandates: rebuilding a tech-debt-laden sales data foundation alongside data engineering peers, and transforming the team from reactive query fulfillment toward rigorous forecasting, causal inference, and statistical thinking. The leader will serve as a strategic partner to senior revenue, operations, and engineering stakeholders while developing a team of scientists and analysts.

Responsibilities

- Set vision for and lead a team of data scientists and analysts delivering impact across customer acquisition and product expansion sales motions. - Conduct deep-dive analyses to surface revenue drivers and anomalies, translating complex findings into clear, actionable recommendations for senior leadership and cross-functional partners. - Partner with data engineering and platform teams to assess current systems, architect a modern scalable sales data layer, and execute a phased remediation. - Align with senior revenue, sales operations, and platform engineering leaders on a shared data and infrastructure roadmap. - Evolve forecasting methodology by incorporating more rigorous approaches such as propensity scoring and improved seasonality controls. - Leverage AI-first development to build self-service analytics capabilities for operations partners, while recruiting, developing, and coaching team talent to raise analytical rigor across the function.

Requirements

- 10+ years of experience in data science or related fields, with at least 4 years leading a growing analytics team. - Builder-minded leader who develops people and maintains a roll-up-your-sleeves attitude, able to step into details to build reports, run analyses, and troubleshoot when needed. - Strong command of experimental design, causal reasoning, and quasi-experimental methods (e.g., difference-in-differences, synthetic control, regression discontinuity, propensity score matching), including the ability to communicate tradeoffs of observational causal claims to non-technical stakeholders. - Deep experience working with CRM data at scale, including extraction strategies, warehouse reconciliation, and treating CRM systems 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. - Strong SQL skills and hands-on technical depth, plus a track record of building analytics functions in a high-growth SaaS or technology environment.

Nice to have

- Experience applying AI tooling to analytics workflows and building self-service data products for operations partners. - Background supporting both customer acquisition and expansion sales teams in a fast-scaling subscription business.

Benefits and work setup

- Competitive base salary, benefits, and equity (RSUs), with final offer determined by experience and location. - Hybrid work model with physical offices in Denver, San Francisco, and New York City; in-office expectation approximately 2-3 days per week depending on role. - Secure, reliable internet connection required for remote workdays.