Senior Data Scientist

Snowflake Snowflake · Data AI · CA-Menlo Park, United States · Finance

Senior Data Scientist role focused on building and deploying production-grade forecasting systems for core financial metrics at Snowflake. The role involves applying advanced time-series and probabilistic ML techniques, contributing to internal tooling, establishing best practices for model evaluation, and mentoring other data scientists. It operates at the intersection of machine learning, statistical research, and corporate finance.

What you'd actually do

  1. Design and implement advanced time-series and probabilistic models (e.g., hierarchical models, state-space models, Bayesian approaches, multivariate forecasting).
  2. Contribute to internal tooling and shared infrastructure that enables scalable forecasting and analytics.
  3. Establish best practices for model evaluation, backtesting, uncertainty quantification, and scenario simulation.
  4. Apply advanced statistical and ML techniques to model customer behavior, product adoption, revenue dynamics, and cost trends.
  5. Drive improvements in automation, monitoring, drift detection, and lifecycle management of forecasting models.

Skills

Required

  • Advanced degree in a quantitative discipline (Statistics, Mathematics, Operations Research, Economics, Engineering, Computer Science) or equivalent practical experience.
  • 8+ years of experience building and deploying production-grade ML or statistical systems, with significant experience in time-series modeling.
  • Deep expertise in probabilistic modeling, forecasting methodologies, and model evaluation techniques.
  • Strong proficiency in Python and the scientific Python ecosystem; fluency in SQL.
  • Experience designing systems for large-scale data processing (e.g., Snowflake, BigQuery, Redshift, Spark).
  • Demonstrated ability to lead technically ambiguous projects with significant business impact.
  • Excellent communication skills, with experience presenting complex quantitative findings to executive stakeholders.
  • A track record of elevating technical standards and mentoring other scientists.

What the JD emphasized

  • 8+ years of experience building and deploying production-grade ML or statistical systems
  • Deep expertise in probabilistic modeling, forecasting methodologies, and model evaluation techniques.
  • Demonstrated ability to lead technically ambiguous projects with significant business impact.
  • A track record of elevating technical standards and mentoring other scientists.

Other signals

  • building production-grade forecasting systems
  • modeling customer behavior, product adoption, revenue dynamics, and cost trends
  • drive improvements in automation, monitoring, drift detection, and lifecycle management of forecasting models