PandaDoc provides a digital platform that automates the creation, sending, signing, and management of business documents such as quotes, contracts, and invoices.
<h2><strong>The Opportunity</strong></h2> <p>As a Senior GTM Data Scientist at PandaDoc, you will be a critical analytical partner to our Go-To-Market (GTM) teams. You will embed yourself in our GTM data to uncover insights and drive actionable recommendations across Sales, Marketing, and Customer Success.</p> <p>The core of this role is to design, build, and maintain predictive machine learning models that optimize customer acquisition, revenue attribution, and retention efforts. You will apply analytical rigor and methodologies like experimentation and causal inference to provide GTM leadership with a reliable understanding of business efficiency and impact. You will report to the Director of GTM Data and act as a reliable thought partner to Marketing, Sales, Customer Success, and Finance.</p> <h2><strong>What You'll Do</strong></h2> <h3><strong>Predictive Modeling & GTM Strategy</strong></h3> <ul> <li><strong>Model Development:</strong> Design, build, and deploy foundational GTM models, including Customer Lifetime Value (LTV) forecasting, Marketing and Sales Attribution, and Propensity models (e.g., propensity to convert, churn, or expand).</li> <li><strong>GTM Experimentation:</strong> Partner with GTM teams to design and analyze controlled experiments across various channels, including website A/B testing, pricing experiments, and marketing campaign effectiveness. You will use methodologies such as AB, multivariate, Bayesian, and Causal Inference.</li> <li><strong>Deep Dive Analysis:</strong> Execute proactive, complex analytical deep dives to discover latent user behavior and root causes of changes in GTM metrics, translating findings into actionable recommendations.</li> <li><strong>Marketing Mix Modeling (MMM):</strong> Support the interpretation of MMM results to help maximize marketing ROI and assess the feasibility of future in-house modeling.</li> </ul> <h3><strong>Measurement & Technical Rigor</strong></h3> <ul> <li><strong>Measurement Frameworks:</strong> Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps GTM activities (e.g., CAC, Funnel conversion, Retention) to high-level business outcomes.</li> <li><strong>Data Advocacy:</strong> Translate complex statistical findings and model outputs into compelling business narratives for cross-functional partners.</li> <li><strong>Data Partnership:</strong> Work closely with Data Engineering to ensure data quality, reliable instrumentation, and the development of reusable predictive assets like model feature stores.</li> <li><strong>Guidance:</strong> Provide technical guidance to peers and stakeholders on best practices for data exploration, ML modeling, and causal methodologies.</li> </ul> <h2><strong>About You</strong></h2> <h3><strong>Qualifications</strong></h3> <ul> <li><strong>Experience:</strong> 4+ years of professional experience in an applied data science, economics, or GTM analytics role, with a proven track record of leveraging predictive modeling and experimentation to drive measurable business impact.</li> <li><strong>Education:</strong> B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline. A Master’s degree is preferred, but not required</li> </ul> <h3><strong>Technical Expertise</strong></h3> <ul> <li><strong>Machine Learning:</strong> Demonstrated experience in building and validating production-ready models for business applications (LTV, Attribution, Propensity).</li> <li><strong>Causal Inference:</strong> Practical application of Causal Inference methods, such as Quasi-Experimentation, Matching Methods (PSM), and Difference-in-Differences</li> <li><strong>Experimentation:</strong> Proficiency in statistical methodologies for A/B testing, including sample size calculations, sequential testing, and variance reduction techniques.</li> <li><strong>Programming & Tools:</strong> Advanced proficiency in Python or R (specifically Scikit-Learn, pandas, numpy) and expert-level SQL.</li> <li><strong>Data Pipelining:</strong> Experience with tools like dbt, Airflow, Databricks, or Snowflake is a strong plus.</li> </ul> <h3><strong>Key Attributes</strong></h3> <ul> <li><strong>Strategic Communication:</strong> Strong data storytelling skills with the ability to influence cross-functional partners and drive consensus in ambiguous environments.</li> <li><strong>Thrive in Ambiguity:</strong> Ability to translate complex business questions into clear analytical frameworks while managing multiple competing priorities.</li> <li><strong>Domain Expertise:</strong> Experience in a SaaS domain and a strong focus on supporting Sales, Marketing, or Customer Success data needs are highly preferred. Experience building LTV, attribution, and propensity models is strongly preferred.</li> </ul> <h2><strong>Company Culture: </strong></h2> <ul> <li>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.</li> <li>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. </li> <li>Check out our<a href="https://www.linkedin.com/company/pandadoc/life/pdcompanyculture"> LinkedIn</a> to learn more. </li> </ul> <p><strong>Benefits</strong></p> <ul> <li><strong>Competitive salary </strong>(If you are located in Poland the salary range is 24,000 to 29,000 PLN gross per month)</li> <li><strong>Remote-first approach</strong> with the option for hybrid work from our offices in Kyiv, Warsaw, and Lisbon.</li> <li><strong>We value long-term collaboration</strong>, whether through typical employment contract, employment of record or B2B arrangements. Be aware that <strong>contract type and benefits vary by location</s