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Snowflake is hiring a Staff Data Scientist to lead the next phase of its driver-based revenue modeling program within the Finance Data Science team. This role owns high-impact, open-ended problems spanning driver identification, revenue decomposition, leading indicators, cohort and use-case modeling, scenario analysis, and multi-year forecasting.
You will scale a standardized driver-based revenue modeling framework across Snowflake's product categories, building on initial work and extending it to AI/ML, Analytics, and other workloads. Key responsibilities include defining clear driver trees, attribution rules, measurement standards, and taxonomies that connect customer adoption, workload volume, usage intensity, unit economics, pricing, and cohorts to revenue outcomes.
The role requires developing statistical, econometric, and machine learning methods to identify leading indicators, estimate causal relationships, and quantify substitution or complementary effects. You will forecast key drivers and revenue across short- and long-range horizons using direct, driver-based, cohort, hierarchical, probabilistic, or blended approaches. You'll build self-service scenario, decomposition, and what-if tools with monthly and multi-year views by workload, region, theater, and cohort to help Product and Finance leaders understand forecast movements and quantify actions needed to achieve revenue targets.
You will establish high standards for point-in-time evaluation, backtesting, stability testing, forecast reconciliation, confidence intervals, and documented model changes. Productionizing frequently refreshed pipelines and applications with strong data-quality gates, monitoring, anomaly detection, versioning, and safe lifecycle management is essential. Close partnership with Product Finance, Product Data Science, Finance Data and Analytics, Analytics Engineering, Product, and go-to-market teams is required to resolve data gaps and validate assumptions.
At the Staff level, you will set cross-category direction, influence the broader modeling roadmap, and raise technical rigor standards through mentorship and technical leadership. You'll communicate clearly with senior leaders about forecast drivers, key assumptions, uncertainty, risks, and implications for product prioritization and resource allocation.
