Staff Data Scientist (Finance)

TL;DR

Staff Data Scientist (Finance): Building and scaling driver-based revenue modeling platforms to translate product activity into financial outcomes with an accent on causal inference, time-series forecasting, and production-grade decision systems. Focus on developing statistical and ML methods to identify leading indicators and automating scenario planning for executive decision-making.

Location: Hybrid (Menlo Park or Dublin, CA, USA)

Salary: $184,000 – $264,500

Company

Snowflake is a leading data cloud company powering the agentic enterprise era through AI-native solutions.

What you will do

• Scale a standardized driver-based revenue modeling framework across product categories, including AI/ML and Analytics.
• Define driver trees, attribution rules, and taxonomies connecting customer adoption and workload volume to revenue.
• Develop statistical, econometric, and ML methods to identify leading indicators and quantify causal relationships.
• Build self-service scenario and "what-if" tools for monthly and multi-year revenue forecasting.
• Productionize frequently refreshed pipelines with strong monitoring, anomaly detection, and versioning.
• Partner with Product Finance, Data Science, and GTM teams to validate assumptions and resolve data gaps.

Requirements

• Advanced degree in Statistics, Mathematics, Economics, CS, or a related quantitative field.
• 5+ years of experience building production-grade statistical or ML forecasting systems with meaningful business impact.
• Proficiency in Python and SQL for manipulating large datasets and building reproducible analyses.
• Experience with large-scale data platforms such as Snowflake, BigQuery, Redshift, or Spark.
• Strong expertise in time-series forecasting, causal inference, and panel/cohort methods.
• Must be based in the USA and able to work in a hybrid model from Menlo Park or Dublin, CA.

Nice to have

• Experience in consumption-based or hybrid SaaS business modeling.
• Background in executive-facing product finance or multi-year planning.
• Experience building analytical applications used in recurring planning and operating cadences.
• Track record of mentoring other scientists and shaping production standards.

Culture & Benefits

• Opportunity to work in an AI-native environment where AI is treated as a core collaborator.
• Collaborative, low-ego culture that values an experimental mindset and rapid testing.
• High-impact role directly influencing executive decision-making and long-term corporate strategy.
• Dynamic and fast-moving work environment with a focus on innovation.