Senior GTM Data Scientist

PandaDoc

Senior GTM Data Scientist at PandaDoc, leveraging predictive modeling to optimize customer acquisition and revenue attribution. Collaborating with GTM teams and providing actionable insights from data analysis.

Posted 7/7/2026full-timeRemote • 🇺🇸 United StatesSenior💰 PLN 24,000 - PLN 29,000 per monthWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in predictive modeling, causal inference, and statistical methodologies to drive business impact through data analysis and experimentation. Proficient in translating complex data findings into actionable insights for cross-functional teams.

Highest-signal resume keywords

Predictive ModelingCausal Inference MethodsA/B Testing MethodologiesPython or R ProficiencySQL Expertise

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Customer Lifetime Value ForecastingMarketing AttributionPropensity ModelingStatistical AnalysisData AnalysisModel ValidationSample Size CalculationVariance Reduction TechniquesControlled Experiment DesignKPI Framework Development

Soft Skills

Analytical ThinkingCommunication SkillsCollaboration

Tools & Technologies

Scikit-LearnPandasNumpyDbtAirflowDatabricksSnowflake

Industry Keywords

GTM AnalyticsSaaS DomainMarketing Mix ModelingSales DataCustomer Success Data

Tech Stack

Tools & technologies

AirflowNumpyPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Design, build, and deploy foundational GTM models, including Customer Lifetime Value (LTV) forecasting, Marketing and Sales Attribution, and Propensity models.
Partner with GTM teams to design and analyze controlled experiments across various channels, including website A/B testing, pricing experiments, and marketing campaign effectiveness.
Execute proactive, complex analytical deep dives to discover latent user behavior and root causes of changes in GTM metrics, translating findings into actionable recommendations.
Support the interpretation of Marketing Mix Modeling (MMM) results to help maximize marketing ROI and assess the feasibility of future in-house modeling.
Define, instrument, and govern a unified Key Performance Indicator (KPI) framework that maps GTM activities to high-level business outcomes.
Translate complex statistical findings and model outputs into compelling business narratives for cross-functional partners.
Work closely with Data Engineering to ensure data quality, reliable instrumentation, and the development of reusable predictive assets like model feature stores.
Provide technical guidance to peers and stakeholders on best practices for data exploration, ML modeling, and causal methodologies.

Requirements

What you’ll need

4+ years of professional experience in an applied data science, economics, or GTM analytics role.
A proven track record of leveraging predictive modeling and experimentation to drive measurable business impact.
B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline.
Experience in building and validating production-ready models for business applications (LTV, Attribution, Propensity).
Practical application of Causal Inference methods, such as Quasi-Experimentation, Matching Methods (PSM), and Difference-in-Differences.
Proficiency in statistical methodologies for A/B testing, including sample size calculations, sequential testing, and variance reduction techniques.
Advanced proficiency in Python or R (specifically Scikit-Learn, pandas, numpy) and expert-level SQL.
Experience with tools like dbt, Airflow, Databricks, or Snowflake is a strong plus.
Experience in a SaaS domain and a strong focus on supporting Sales, Marketing, or Customer Success data needs are highly preferred.

Benefits

Comp & perks

Competitive salary (If you are located in Poland the salary range is 24,000 to 29,000 PLN gross per month)
Remote-first approach with the option for hybrid work from our offices in Kyiv, Warsaw, and Lisbon.
We value long-term collaboration, whether through typical employment contract, employment of record or B2B arrangements.
Work schedule aligned with EU time zones.
Honest, open culture that values constructive feedback.
Professional and personal development within a collaborative, supportive team.
Stable yet growing SaaS product offering an agile environment, ownership, start-up energy, and strong technical challenges.