Senior Data Scientist - GTM Data

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. 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. Experience building LTV, attribution, and propensity models is strongly preferred.

Benefits

• Annual base salary up to $165,000-$185,000.
• Tremendous career growth opportunities, competitive salary, health and commuter benefits, company paid life & disability, 20+ PTO days, 401K and FSA plans.