• Work remotely in support of a government contract for a client located in Washington, DC. • Collaborate on technical solutions and lead implementation efforts. • Develop and engineer data workflows to merge disparate data sources. • Design and develop automation for security tools management. • Apply analytical rigor and statistical methods to analyze large amounts of data.

• Work remotely in support of a government contract for a client located in Washington, DC. • Develop and support data-oriented tasks for cybersecurity solutioning. • Architect and design analytic methods using machine learning.

• Conduct in-depth data analysis and build advanced statistical models to extract insights from large viewing and demographic datasets. • Develop, train, and deploy state-of-the-art machine learning models to solve a variety of measurement problems. • Work with our Engineering teams to design and implement efficient data pipelines to collect, process, and transform data from various sources. • Stay up-to-date with the latest data science techniques, tools, and technologies, and explore novel approaches to solve complex challenges.

• 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). • 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. • 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. • 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. • 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. • 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 empower other analysts and data consumers. • Strategic Influence: Act as a strategic thinker by translating complex statistical findings into clear, compelling, and actionable business narratives for cross-functional partners and senior leadership (VP/C-suite), driving strategic decisions and investment priorities. • Mentorship and Training: Serve as a technical subject matter expert, training and mentoring junior and mid-level data scientists on best practices in statistical rigor, experimental design, and causal modeling.