Head of Sales Data Science & Analytics

United States Digital Space LLC; Denver (NY); On-site; USD 190,000 - 230,000; Full time

Job summary

United States Digital Space LLC is seeking a Senior Data Science Leader to empower the Sales Data team that fuels insights, forecasting, and revenue analytics. You will guide senior sales and marketing leaders, mentor analysts, and collaborate across Sales Operations, Data Engineering, Finance, and R&D to transform data into strategic impact.

The role emphasizes building strong data foundations, moving from reactive reporting to advanced forecasting, and embedding AI-powered self-service

Qualifications

• 10+ years of experience in data science or related fields.
• 4+ years leading a growing analytics team.
• Leadership with a Builder Mindset and hands-on execution.
• Expertise in statistical and causal inference methods.

Responsibilities

• Lead the Sales Analytics team and drive impact across revenue growth.
• Conduct deep-dive analyses to identify revenue drivers and anomalies; translate insights for leadership.
• Rebuild and evolve the data foundation with Data Engineering for scalable analytics.
• Partner with CRO, Sales Ops, Data Eng, and Service Platform to align data roadmaps.
• Advance forecasting capabilities with rigorous analytics, causal inference, and experimentation.

Skills

Leadership

SQL

Salesforce data

dbt

Snowflake

Statistical inference

Causal inference

Analytics leadership

Tools

dbt

Snowflake

Salesforce

Job description

About the company

At the company, we're on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.

All full-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build the company should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy.

AI is a fundamental part of how work gets done at the company. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.

About the Role:

the company 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 the company'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.

This role has two equally important mandates. The first is operational:

the company'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.

The second is transformational:

as the foundation gets rebuilt, this leader must simultaneously evolve what the team does with it: moving from a function defined by query fulfillment and reactive reporting toward one defined by advanced forecasting capabilities, causal inference, and analytical rigor. The ideal candidate brings deep expertise in Sales data, a commitment to building strong foundations for scale, paired with a genuine command of experimentation and quasi-experimental methods, and can instill these capabilities across the team.

Here's what you'll do day-to-day:

• Lead the Sales Analytics team -- Drive vision for and champion a team of data scientists and analysts to deliver impact across customer acquisition and product expansion sales teams.
• Insights and Recommendations: 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, drive the evolution of our foundational data systems needed to build a world class sales analytics ecosystem.
• Partner with key stakeholders -- work closely with the Chief Revenue Officer, Sales Operations, Data Engineering, and the Service Platform team to align on a shared data and infrastructure roadmap.
• Evolve forecasting capabilities -- Evolve our sales forecasting methodology to incorporate more rigorous analytics methods (eg propensity scoring, better seasonality controls, etc)
• Enable self-service analytics with AI -- leverage the company'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.
• Drive team excellence -- Recruit, mentor, and develop a high-performing, proactive analytics team, shifting the culture from reactive query fulfillment toward strategic analysis. As AI handles more of the routine reporting, define what excellence looks like for analysts in a world where causal thinking and statistical fluency are the new baseline.
• 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.

What we're looking for:

• 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.
• Statistical and causal inference expertise -- 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 -- deep experience working with Salesforce data at scale, including understanding data extraction strategies, CRM-to-warehouse reconciliation, and the challenges of treating Salesforce as a source of truth.
• Infrastructure-first mindset -- 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.
• Technical depth -- 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.
• Sales and revenue domain knowledge -- 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.
• Cross-functional partnership -- 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.
• Change management and team development -- experience leading teams through significant transformation, raising performance expectations, coaching analysts toward more strategic work, and making tough talent decisions when needed.
• Strong communication skills -- experience presen