Staff Data Scientist, Finance

The Finance Data Science team builds the forecasting and decision systems that power Snowflake's financial planning, operating cadence, and long-term strategy. Our work informs executive decision-making, product and go-to-market priorities, resource allocation, pricing, and cross-functional decisions across Finance, Product, Sales, and Data Science.

We are expanding a driver-based revenue modeling platform that translates product and workload activity into trusted financial outcomes. The program began with one product category and will scale a common modeling and publishing framework across Snowflake's product categories. The models are highly visible, refreshed frequently, and designed for self-service scenario planning and business reviews.

We are hiring a Staff Data Scientist to lead the next phase of Snowflake's driver-based revenue modeling program. This role is not just about building models. It is about creating reliable, explainable, production-grade decision systems that connect upstream business and product levers to revenue outcomes.

You will own 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 build on the initial category model and extend and adapt the approach to additional product categories, partnering closely with Product Finance, Product Data Science, Product leaders, go-to-market teams, Analytics Engineering, and Finance Data and Analytics.

This role is well suited for someone who combines modeling depth, causal and business reasoning, production rigor, and a high sense of ownership.

- 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.

- 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 experience with 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.

- 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 used in recurring planning and operating cadences.

- Experience mentoring scientists and shaping shared modeling, experimentation, data-quality, or production standards.

In this role, success means Snowflake leaders can trace revenue forecasts to a small set of measurable product and business drivers, understand why results changed, and run credible scenarios without bespoke analyst support. You balance modeling sophistication with business practicality, scale a common framework across categories without forcing false uniformity, and operate systems that are accurate, explainable, monitored, versioned, and trusted.

Over time, the models become a durable operating mechanism for product prioritization, go-to-market accountability, and financial planning.