Staff Data Scientist, Finance
Snowflake · US-CA-Menlo Park
About this role
**Staff Data Scientist, Finance** **About the Team** The Finance Data Science team builds forecasting and decision systems that power Snowflake's financial planning, operating cadence, and long-term strategy. Work informs executive decision-making, product priorities, resource allocation, pricing, and cross-functional decisions across Finance, Product, Sales, and Data Science. **The Role** Lead the next phase of Snowflake's driver-based revenue modeling program. Own high-impact problems spanning driver identification, revenue decomposition, leading indicators, cohort modeling, scenario analysis, and multi-year forecasting. Build reliable, explainable, production-grade decision systems connecting business and product levers to revenue outcomes. **Key Responsibilities** • Scale a standardized driver-based revenue modeling framework across product categories • Define driver trees, attribution rules, measurement standards, and taxonomies • Develop statistical, econometric, and ML methods for leading indicators and causal relationships • Forecast key drivers and revenue across short- and long-range horizons • Build self-service scenario and what-if tools for Product and Finance leaders • Establish high standards for evaluation, backtesting, and model monitoring • Productionize frequently refreshed pipelines with strong data-quality gates • Partner with Product Finance, Data Science, Analytics Engineering, and go-to-market teams • Communicate clearly with senior leaders about forecast drivers and implications • Mentor and set technical direction across teams **Required Qualifications** • Advanced degree in Statistics, Mathematics, Operations Research, Economics, Engineering, CS, or equivalent experience • 5+ years building and operating production-grade statistical, forecasting, or ML systems with business impact • Strong experience with business-critical forecasting, driver-based modeling, or financial planning • Deep modeling skills: time-series forecasting, causal inference, panel/cohort methods, hierarchical models • Proficiency in Python and SQL with ability to manipulate large datasets • Experience with large-scale data systems (Snowflake, BigQuery, Redshift, Spark) • Strong systems thinking: monitoring, validation, versioning, reproducibility • Demonstrated ownership of high-stakes outputs for executive stakeholders • Excellent communication and influence skills **Especially Valuable** • Modeling in consumption-based or usage-based SaaS • Executive-facing product finance or multi-year planning experience • Product telemetry and customer cohort analysis • Building self-service analytical applications • Mentoring and shaping modeling standards
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