# Staff Data Scientist, Finance

**Company:** [Snowflake](https://hotfix.jobs/companies/snowflake)
**Location:** Menlo Park, CA, California, Dublin, CA
**Role:** Data Science
**Salary:** $184k – $265k/yr
**Experience:** 7+ years
**Skills:** Python, SQL, Statistics, time-series forecasting, Causal Inference, econometrics, Machine Learning, cohort modeling, hierarchical models, probabilistic models, Snowflake, BigQuery, Redshift, Spark, Anomaly Detection
**Posted:** 2026-08-05

> Leads production-grade forecasting and driver-based revenue modeling across product categories, translating product usage and business drivers into explainable financial outcomes. Requires 5+ years of experience with forecasting, causal or econometric modeling, Python, SQL, large-scale data systems, and executive-facing decision products.

## Job Description

## 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.

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