Staff GTM Data Scientist
PandaDoc
full-time
Posted on:
Location Type: Remote
Location: Anywhere in Europe
Visit company websiteExplore more
Job Level
About the role
- 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.
- 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).
- Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods.
- Apply advanced causal inference techniques to scenarios where randomized controlled trials (RCTs) are infeasible.
- 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.
- Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps low-level product health metrics to high-level business outcomes.
Requirements
- 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.
- 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.
- 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.
- 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.
- 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.
- Advanced proficiency in Python or R for statistical modeling, with experience using relevant data science packages (e.g., SciKit-Learn, numpy, pandas).
- Expert-level proficiency in SQL and experience working with established data warehouses (e.g., Snowflake, Postgres).
- Experience with data transformation and workflow management tools such as dbt, Airflow, or Databricks is a strong plus.
- 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.
- Proven ability to drive organizational change management in environments where experimentation and data-driven decision-making are not yet widely adopted.
- Ability to navigate significant ambiguity, translate complex business questions into clear analytical frameworks, and manage multiple competing priorities in a fast-paced environment.
- Experience in a SaaS domain and a strong focus on Product Data Science are strongly preferred.
Benefits
- 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!
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data sciencecausal inferenceA/B testingstatistical modelingtime-series analysisquantitative methodsdata analysisexperimental designsample size calculationsvariance reduction techniques
Soft Skills
communicationpresentationdata storytellinginfluencingchange managementnavigating ambiguityanalytical thinkingprioritizationcollaborationleadership