Senior Data Scientist - GTM Data

The Opportunity 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. 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. What You'll Do Predictive Modeling & GTM Strategy Model Development: 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). GTM Experimentation: 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. Deep Dive Analysis: Execute proactive, complex analytical deep dives to discover latent user behavior and root causes of changes in GTM metrics, translating findings into actionable recommendations. Marketing Mix Modeling (MMM): Support the interpretation of MMM results to help maximize marketing ROI and assess the feasibility of future in-house modeling. Measurement & Technical Rigor Measurement Frameworks: 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. Data Advocacy: Translate complex statistical findings and model outputs into compelling business narratives for cross-functional partners. Data Partnership: Work closely with Data Engineering to ensure data quality, reliable instrumentation, and the development of reusable predictive assets like model feature stores. Guidance: Provide technical guidance to peers and stakeholders on best practices for data exploration, ML modeling, and causal methodologies. About You Qualifications Experience: 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. Education: 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 Technical Expertise Machine Learning: Demonstrated experience in building and validating production-ready models for business applications (LTV, Attribution, Propensity). Causal Inference: Practical application of Causal Inference methods, such as Quasi-Experimentation, Matching Methods (PSM), and Difference-in-Differences Experimentation: Proficiency in statistical methodologies for A/B testing, including sample size calculations, sequential testing, and variance reduction techniques. Programming & Tools: Advanced proficiency in Python or R (specifically Scikit-Learn, pandas, numpy) and expert-level SQL. Data Pipelining: Experience with tools like dbt, Airflow, Databricks, or Snowflake is a strong plus. Key Attributes Strategic Communication: Strong data storytelling skills with the ability to influence cross-functional partners and drive consensus in ambiguous environments. Thrive in Ambiguity: Ability to translate complex business questions into clear analytical frameworks while managing multiple competing priorities. Domain Expertise: Experience in a SaaS domain and a strong focus on supporting Sales, Marketing, or Customer Success data needs are highly preferred. Exp