Senior GTM Data Scientist

What You'll Do

Predictive Modeling & GTM Strategy

• 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 channels including website A/B testing, pricing experiments, and marketing campaign effectiveness using methodologies such as AB, multivariate, Bayesian, and Causal Inference.
• 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 maximize marketing ROI and assess feasibility of future in-house modeling.

Measurement & Technical Rigor

• Define, instrument, and govern a unified 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 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.

About You

Qualifications

• 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. Master’s degree preferred.

Technical Expertise

• Demonstrated experience 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 (Scikit-Learn, pandas, numpy) and expert-level SQL.
• Experience with tools like dbt, Airflow, Databricks, or Snowflake is a strong plus.

Key Attributes

• Strong data storytelling skills to influence cross-functional partners and drive consensus in ambiguous environments.
• Ability to translate complex business questions into clear analytical frameworks while managing multiple priorities.
• Experience in a SaaS domain supporting Sales, Marketing, or Customer Success is highly preferred. Experience building LTV, attribution, and propensity models strongly preferred.

Benefits

• The annual OTE for this role is up to $185k.
• Remote-first approach.