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Snowflake is seeking a Staff Data Scientist to lead the technical direction of AI and machine learning systems powering go-to-market (GTM) decisions across sales and marketing. This is a high-impact individual contributor role within the Data Analytics and AI organization, responsible for defining how decision systems are designed, evaluated, productionized, and integrated into seller, marketer, and business leader workflows.
Key responsibilities include: setting technical direction for a portfolio of GTM ML/AI systems; developing pipeline forecasting models that predict stage progression, conversion, and deal timing; building account, lead, opportunity, and customer propensity and risk models; creating recommendation and next-best-action systems; applying causal inference and experimentation to measure incremental impact of GTM interventions; establishing reusable technical standards for model training, backtesting, calibration, and evaluation; partnering with GTM leaders and RevOps to identify high-value decisions and embed outputs into workflows; and mentoring other data scientists.
Required qualifications: advanced degree in Statistics, Mathematics, Operations Research, Economics, Engineering, Computer Science, or equivalent practical experience; 5+ years building production-grade statistical or ML systems with business impact; demonstrated ability to set technical direction across ambiguous, cross-functional problem spaces; deep expertise in causal inference, experimentation, forecasting, propensity modeling, uplift modeling, ranking, or recommendation systems; strong judgment about when to apply predictive ML, causal methods, generative AI, or simpler approaches; experience translating business decisions into measurable objectives and production systems; strong Python and SQL skills; experience with large-scale data platforms and model lifecycle management (monitoring, validation, versioning, reproducibility); ability to work with imperfect CRM and marketing data while making assumptions explicit; demonstrated ownership of high-stakes outputs for business stakeholders; and excellent communication and cross-functional influence skills.
Highly valued: B2B SaaS and enterprise sales experience; pipeline forecasting, account prioritization, lead/opportunity scoring, expansion, renewal, or churn modeling; incrementality testing and marketing effectiveness measurement; recommendation systems and decision optimization; LLM and AI-assisted sales/marketing workflows; familiarity with CRM, marketing automation, product telemetry, and customer success data; and experience deploying model outputs into business workflows and measuring adoption.
