Senior GTM Data Scientist

About the role

This position offers a chance to be a key analytical contributor to PandaDoc's Go-To-Market (GTM) functions. You will analyze GTM data to provide actionable insights and recommendations to Sales, Marketing, and Customer Success teams. The role involves developing predictive models to enhance customer acquisition, revenue tracking, and retention.

Key facts

What you'll do

Develop and implement core GTM models, such as customer lifetime value (LTV) prediction, marketing and sales attribution, and propensity to convert, churn, or expand.

Collaborate with GTM teams to design and evaluate experiments across various channels, including website A/B tests, pricing adjustments, and marketing campaign performance, using methods like A/B testing, multivariate, Bayesian, and causal inference.

Conduct in-depth analyses to understand user behavior and identify the drivers of GTM metric changes, translating findings into practical business suggestions.

Assist in interpreting marketing mix modeling (MMM) results to optimize marketing return on investment and evaluate the potential for in-house modeling.

Establish and maintain a consistent framework for key performance indicators (KPIs) that link GTM activities to overall business objectives.

Communicate complex analytical findings and model outcomes clearly to cross-functional stakeholders.

Work with Data Engineering to ensure data integrity, proper tracking, and the creation of reusable predictive components.

Offer technical guidance to colleagues and stakeholders on data analysis, machine learning, and causal inference best practices.

Requirements

Possess at least 4 years of professional experience in applied data science, economics, or GTM analytics, with a demonstrated history of using predictive modeling and experimentation to achieve business results.

Hold a Bachelor's degree in Mathematics, Statistics, Economics, Computer Science, or a related quantitative field. A Master's degree is advantageous.

Demonstrate experience building and validating production-ready machine learning models for business applications like LTV, attribution, and propensity.

Have practical experience applying causal inference techniques, including quasi-experimentation, matching methods (PSM), and difference-in-differences.

Show proficiency in statistical methods for A/B testing, including sample size calculations, sequential testing, and variance reduction.

Exhibit advanced skills in Python or R (including libraries like Scikit-Learn, pandas, numpy) and expert-level SQL.

Nice to have

Experience with data pipelining tools such as dbt, Airflow, Databricks, or Snowflake.

Experience in a SaaS environment, particularly supporting Sales, Marketing, or Customer Success data needs.

Prior experience building LTV, attribution, and propensity models.

Skills & tools

Python, R, Scikit-Learn, pandas, numpy, SQL, dbt, Airflow, Databricks, Snowflake

Practical notes

This role is remote-first in the USA.

PandaDoc values work-life balance, a supportive team environment, and offers professional development.

The company is committed to equal treatment for all employees.

External recruiters require prior HR approval and should not contact employees directly.