The Opportunity
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.
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.
What You'll Do
Experimentation & Causal Strategy
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Lead the Experimentation Roadmap: 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).
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Advanced Experiment Design: 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.
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Causal Inference Beyond A/B: 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.
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Deep Dive Analysis: 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.
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Develop Measurement Frameworks: 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.
Technical Leadership & Influence
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Scaling Data Science: 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