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Staff Data Scientist, Finance

Snowflake Inc.· Finance
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Hybrid (US-CA-Menlo Park), Hybrid (US-CA-Dublin)$184k - $264.5kFull-timePosted 2mo ago

About the role

Staff Data Scientist to lead the driver-based revenue modeling program in Snowflake's Finance Data Science team, building reliable, production-grade decision systems that connect product levers to revenue outcomes and scale across product categories.

Responsibilities

  • Own and scale a standardized driver-based revenue modeling framework across Snowflake's product categories, building on the Data Engineering model and extending it to AI/ML, Analytics, and other workloads.
  • Define clear driver trees, attribution rules, measurement standards, assumptions, and taxonomies that connect customer adoption, workload volume, usage intensity, unit economics, pricing, and use-case or migration 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 according to the structure and data quality of each workload.
  • Build self-service scenario, decomposition, and what-if tools with monthly and multi-year views by workload, region, theater, and cohort, helping Product and Finance leaders understand forecast beats or misses, compare base and stretch cases, and quantify the actions required to achieve revenue targets.
  • Establish high 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 strong data-quality gates, monitoring, anomaly detection, versioning, reproducible backfills, and safe lifecycle management.
  • Partner closely with Product Finance, Product Data Science, Finance Data and Analytics, Analytics Engineering, Product, and go-to-market teams to resolve data gaps, validate assumptions, and incorporate high-quality business context.
  • Communicate clearly with senior leaders about the drivers behind forecast movements, key assumptions, uncertainty, risks, and implications for product prioritization, go-to-market execution, and resource allocation.
  • Raise the bar for technical rigor and reusable standards through mentorship and technical leadership; at the Staff level, set cross-category direction and influence the broader modeling roadmap.

Qualifications

  • 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. Staff candidates will also have a track record of setting technical direction across broad or multi-team problem spaces.
  • Strong hands-on experience with business-critical forecasting, driver-based or unit-economics modeling, financial planning, demand or capacity planning, or other systems that connect operational inputs to business outcomes.
  • Deep modeling skills, including strong judgment around time-series forecasting, causal inference, panel or cohort methods, segmentation, hierarchical or probabilistic models, and when a simpler approach is more reliable than a more sophisticated one.
  • Ability to work with imperfect or limited telemetry, define defensible assumptions, identify and close data gaps, and distinguish true business movement from instrumentation changes, one-time events, timing shifts, and model artifacts.
  • Strong proficiency in Python and SQL, with the ability to manipulate large data sets, build models, develop reproducible analyses, and productionize them efficiently.
  • Experience working with large-scale data systems and modern data platforms such as Snowflake, BigQuery, Redshift, or Spark.
  • Strong systems thinking, including monitoring, validation, anomaly detection, versioning, reproducibility, backfills, and safe model or pipeline changes in production.
  • Demonstrated ownership of high-stakes outputs used by executive or business stakeholders, including the ability to respond quickly and effectively when data, models, or assumptions change.
  • Excellent communication and influence skills, with a track record of leading through ambiguity, explaining complex relationships and uncertainty, mentoring others, and elevating technical standards across a team.

Skills

Required:Driver-based revenue modelingPythonSQLTime-series forecastingCausal inference / econometricsCommunication to leadership
Last updated Aug 6, 2026. Interested? Apply on the company site.

About Snowflake Inc.

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