GTM Staff Data Scientist

Responsibilities

• Set the technical direction for a portfolio of AI and machine learning GTM decision systems spanning Sales and Marketing.
• Develop pipeline forecasting methods that model stage progression, conversion, and deal timing.
• Build account, lead, opportunity, and customer models that identify propensity, risk, potential, and likely next outcomes.
• Develop recommendation and next-best-action systems that determine where GTM teams should focus, which action to take, and when to take it.
• Apply causal inference, experimentation, and uplift modeling to measure the incremental impact of campaigns, sales activities, and customer interventions.
• Define common standards for point-in-time training, backtesting, calibration, ranking quality, treatment-effect evaluation, uncertainty, and realized business impact.
• Partner with GTM leaders and RevOps to identify high-value decisions, define interventions, and embed outputs into recurring workflows.
• Separate genuine customer and market movement from CRM changes, selection effects, territory shifts, instrumentation gaps, and model artifacts.
• Mentor scientists and raise technical standards across GTM Data Science and partner teams.

Requirements

• Advanced degree in Statistics, Mathematics, Operations Research, Economics, Engineering, Computer Science, or a related quantitative field, or equivalent practical experience.
• 5+ years of experience building production-grade statistical or machine learning systems with meaningful business impact.
• Record of setting technical direction across ambiguous, cross-functional, or multi-team problem spaces.
• Deep expertise in several relevant areas, including causal inference, experimentation, forecasting, propensity modeling, uplift modeling, ranking, or recommendation systems.
• Strong judgment about when to use predictive machine learning, causal methods, generative AI, or a simpler analytical approach.
• Experience translating business decisions into measurable objectives, interventions, evaluation designs, and production systems.
• Strong Python and SQL skills and experience working with large-scale data platforms.
• Experience operating models with monitoring, validation, versioning, reproducibility, and safe lifecycle management.
• Ability to work with imperfect CRM, marketing, product, and customer data while making assumptions and limitations explicit.
• Demonstrated ownership of high-stakes outputs used by business or executive stakeholders.
• Excellent communication, technical leadership, and cross-functional influence skills.

Nice-to-Haves

• Experience with B2B SaaS, enterprise sales, consumption-based businesses, or account-based GTM motions.
• Pipeline forecasting, account prioritization, lead or opportunity scoring, expansion, renewal, or churn modeling.
• Incrementality testing, causal measurement, uplift modeling, or marketing effectiveness.
• Recommendation systems, next-best-action models, ranking, or decision optimization.
• LLMs, agents, retrieval systems, or AI-assisted Sales and Marketing workflows.
• CRM, marketing automation, product telemetry, customer success, and unstructured interaction data.
• Deploying model outputs into business workflows and measuring adoption and realized impact.

Compensation

• Salary range: $184,000–$264,500.