Note: The job is a remote job and is open to candidates in USA. PandaDoc is seeking a Senior GTM Data Scientist to be a critical analytical partner to their Go-To-Market teams. The role involves designing and maintaining predictive machine learning models to optimize customer acquisition and retention efforts, while providing actionable insights to various departments including Sales and Marketing.
Responsibilities
- 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)
- 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 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 (e.g., CAC, Funnel conversion, Retention) 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
Skills
- 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
- B.A. or B.S. in Mathematics, Statistics, Economics, Computer Science, or a related quantitative discipline
- Demonstrated 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
- Strong data storytelling skills with the ability to influence cross-functional partners and drive consensus in ambiguous environments
- Ability to translate complex business questions into clear analytical frameworks while managing multiple competing priorities
- A Master's degree is preferred, but not required
- 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
- Experience with tools like dbt, Airflow, Databricks, or Snowflake is a strong plus
Benefits
- 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. Be aware that contract type and benefits vary by location - feel free to clarify with our recruiters).
- 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.
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