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

In This Role, You Will

• Define and lead the roadmap for GTM Intelligence and Technical Success insight products in partnership with cross-functional leaders.
• Build the data science foundation for Technical Success, including core metrics, customer health definitions, intervention measurement, and reusable playbook analytics.
• Develop propensity score models for model and product feature adoption, helping Technical Success and GTM identify which customers are most likely to adopt, which interventions can move adoption, and where support should focus.
• Build, mentor, and lead a small team of data scientists and cross-functional analytics partners as the GTM Intelligence function scales.
• Set technical standards for modeling, metrics, experimentation, documentation, and production readiness across the team's work.
• Create team operating rhythms that balance urgent field needs with durable roadmap execution, quality review, and stakeholder alignment.
• Build predictive and causal models for customer health, expansion propensity, churn risk, adoption depth, use-case fit, and intervention effectiveness.
• Design next-best-action systems that identify account opportunities and risks, recommend playbooks, and explain the evidence behind each recommendation.
• Partner with Technical Success leaders to enumerate playbooks and actions, instrument action tracking, and measure outcomes over time.
• Develop customer segmentation and benchmarking frameworks across products, industries, personas, support tiers, and lifecycle stages.
• Create scalable insight products that are embedded into field workflows rather than living only as one-off analyses or static dashboards.
• Translate field feedback and account-level patterns into clear product and GTM recommendations for senior leadership.
• Collaborate with Data Engineering and RevOps to improve the data foundations connecting product telemetry, Salesforce, support signals, revenue, and qualitative feedback.
• Maintain a high bar for analytical rigor, including causal evaluation, validation, data quality, and clear caveats.

You Might Thrive in This Role If You Have

• 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.