About the role
<h2><strong>The Opportunity</strong></h2> <p>As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality.</p> <p>You will report to the Director of GTM Data and act as a strategic thought partner to Go-to-Market teams, Marketing, Product, Finance, Design, Engineering, and executive leadership, ensuring alignment between data insights and critical business decisions.</p> <h2><strong>What You'll Do</strong></h2> <h3><strong>Experimentation &amp; Causal Strategy</strong></h3> <ul> <li><strong>Lead the Experimentation Roadmap:</strong> Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV).</li> <li><strong>Advanced Experiment Design:</strong> Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers.</li> <li><strong>Causal Inference Beyond A/B: </strong>Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible.</li> <li><strong>Deep Dive Analysis:</strong> Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends, and root causes of changes in key metrics, translating these findings into actionable product and business insights.</li> <li><strong>Develop Measurement Frameworks:</strong> Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes, ensuring consistent and scalable measurement across the organization.</li> </ul> <h3><strong>Technical Leadership &amp; Influence</strong></h3> <ul> <li><strong>Scaling Data Science:</strong> Partner with Data Engineering to design and build scalable, self-serve experimentation tooling and reusable analytical assets and frameworks (e.g., causal machine learning models) that e