Applied Data Science & Insights Leader - GTM Intelligence Solutions and Technical Success

Highlights

• Build AI-powered intelligence products guiding GTM and Technical Success actions.
• Create next-best-action systems that explain risks and measure interventions.
• Uncover customer happy paths from pilots to durable production adoption.

What they offer

• Equity
• Relocation assistance

Ideal candidate

A hands-on data science leader with 10+ years turning customer and product signals into action, who thrives mentoring teams and partnering with GTM to scale adoption and retention.

Requirements

• 10+ years of experience in applied data science, analytics, machine learning, quantitative strategy, or a closely related field.
• Deep technical skill in SQL and Python, with the ability to move from raw tables to production-quality models, metrics, and decision systems.
• Strong applied experience with statistical modeling, causal inference, machine learning, customer segmentation, churn or health modeling, or recommendation systems.
• Experience with propensity score modeling, uplift modeling, or related causal methods for adoption, activation, retention, or product feature usage.
• Experience building production or workflow-embedded data products for GTM, sales, customer success, technical success, growth, or enterprise SaaS teams.
• Product intuition and business judgment for turning ambiguous questions into repeatable models, tools, metrics, and operating cadences.
• Excellent communication skills, including the ability to distill complex analysis into clear recommendations for technical partners, field teams, and executives.
• Comfort partnering across technical and non-technical teams, including Product, Engineering, Technical Success, Sales, RevOps, Finance, and Data Engineering.
• A track record of operating autonomously in fast-moving environments and raising the quality of how teams use data to make decisions.
• Experience leading teams or serving as a technical lead for multi-person data science initiatives, including mentoring, roadmap-setting, and quality review.
• Ability to hire, develop, and retain strong data science talent while creating a collaborative, high-accountability team culture.
• An advanced degree in a quantitative field, or equivalent practical experience.