Staff GTM Data Scientist

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 Product Data and act as a strategic thought partner to Product, Finance, Design, Engineering, Product Marketing, and executive leadership, ensuring alignment between data insights and critical business decisions.

• 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.

• Experience: 6+ years of professional experience in an applied data science, economics, or product analytics role, with a proven track record of leveraging experimentation and causal inference methods to drive significant business impact.
• Education: B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline. A Master’s degree in a quantitative field (e.g., Statistics, Data Science, Econometrics, Operations Research) is preferred, but not required.

• Causal Inference: Demonstrated expertise in applying a wide range of Causal Inference methods, e.g. Quasi-Experimentation, Matching Methods (PSM), Difference-in-Differences, and/or Instrumental Variables.
• Experimentation Methodologies: Expertise in advanced statistical methodologies for A/B testing, including sample size calculations, sequential testing, dealing with interference/network effects, variance reduction techniques (e.g., CUPED), etc.
• Deep Analytical Methods: Mastery of advanced statistical modeling, time-series analysis, and quantitative methods necessary to perform thorough exploratory data analysis, produce timely insights, and provide actionable recommendations.
• Programming: Advanced proficiency in Python or R for statistical modeling, with experience using relevant data science packages (e.g., SciKit-Learn, numpy, pandas).
• Data Tools: Expert-level proficiency in SQL and experience working with established data warehouses (e.g., Snowflake, Postgres).
• Data Pipelining: Experience with data transformation and workflow management tools such as dbt, Airflow, or Databricks is a strong plus.

• Strategic Communication & Influence: Possesses exceptional communication, presentation, and data storytelling skills with a consistent record of influencing cross-functional partners and leadership at all levels, particularly in navigating and driving consensus in unstructured or ambiguous environments.
• Change Management: Proven ability to drive organizational change management in environments where experimentation and data-driven decision-making are not yet widely adopted.
• Thrive in ambiguity: Ability to navigate significant ambiguity, translate complex business questions into clear analytical frameworks, and manage multiple competing priorities in a fast-paced environment.
• Relevant Experience: Experience in a SaaS domain and a strong focus on Product Data Science are strongly preferred.

• We're known for our work-life balance, kind co-workers, & creative virtual team-bonding events. And although our Pandas are located across the globe, we stay connected with the help of technology and ensure that everyone on our team feels, well, like a team.
• Pandas work best when they're happy. We retain our talent by upholding our values of integrity & transparency, and selling a product that changes the lives of our customers. 
• Check out our LinkedIn to learn more. 

• Our benefits include tremendous career growth opportunities, a competitive salary, health and commuter benefits, company paid life & disability, 20+ PTO days, 401K and FSA plans, and of course, a fun team of Pandas to work with!

PandaDoc is an Equal Opportunity Employer. We are committed to equal treatment of all employees without regard to race, national origin, religion, gender, age, sexual orientation, veteran status, physical or mental disability or other basis protected by law.