Staff Data Scientist, Finance

Responsibilities

• Own and scale a standardized driver-based revenue modeling framework across Snowflake's product categories.
• Define driver trees, attribution rules, measurement standards, assumptions, and taxonomies connecting customer adoption, workload volume, usage intensity, unit economics, pricing, and cohorts to revenue.
• Develop statistical, econometric, and machine learning methods to identify leading indicators, estimate lagged and causal relationships, quantify substitution or complementary effects, and separate signal from telemetry or model artifacts.
• Forecast key drivers and revenue across short- and long-range horizons using direct, driver-based, cohort, hierarchical, probabilistic, or blended approaches.
• Build self-service scenario, decomposition, and what-if tools with monthly and multi-year views by workload, region, theater, and cohort.
• Establish standards for point-in-time evaluation, backtesting, stability testing, forecast reconciliation, confidence intervals, attribution, and documented model or assumption changes.
• Productionize and operate frequently refreshed pipelines and applications with data-quality gates, monitoring, anomaly detection, versioning, reproducible backfills, and safe lifecycle management.
• Partner with Product Finance, Product Data Science, Finance Data and Analytics, Analytics Engineering, Product, and go-to-market teams to resolve data gaps and validate assumptions.
• Communicate forecast drivers, assumptions, uncertainty, risks, and implications to senior leaders.
• Mentor scientists and provide technical leadership across modeling standards and roadmap direction.

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 and operating production-grade statistical, forecasting, econometric, or machine learning systems with meaningful business impact.
• Experience with business-critical forecasting, driver-based or unit-economics modeling, financial planning, demand or capacity planning, or systems connecting operational inputs to business outcomes.
• Deep modeling expertise in time-series forecasting, causal inference, panel or cohort methods, segmentation, and hierarchical or probabilistic models.
• Ability to work with imperfect or limited telemetry, define defensible assumptions, identify data gaps, and distinguish business movement from instrumentation changes, one-time events, timing shifts, and model artifacts.
• Strong proficiency in Python and SQL.
• Experience with large-scale data systems and modern data platforms.
• Experience with monitoring, validation, anomaly detection, versioning, reproducibility, backfills, and safe production changes.
• Experience owning high-stakes outputs used by executive or business stakeholders.
• Excellent communication, influence, mentoring, and technical leadership skills.

Nice to Haves

• Experience modeling or forecasting in a consumption-based, usage-based, or hybrid SaaS business.
• Experience with executive-facing product finance, multi-year planning, revenue forecasts, or business review systems.
• Experience using product telemetry, workload or feature attribution, customer cohorts, migrations, or use cases to explain and forecast business outcomes.
• Experience building self-service scenario tools, analytical applications, or decision products.
• Experience mentoring scientists and shaping shared modeling, experimentation, data-quality, or production standards.