Senior Data Scientist

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

Senior Data Scientist role focused on building and operating production forecasting systems for financial metrics like revenue and bookings in a consumption-based business. The role involves developing scalable statistical and machine learning models, ensuring forecast accuracy, stability, and explainability, and partnering with various teams to improve forecast quality and operational trust. Requires strong modeling depth, production rigor, and ownership.

What you'd actually do

  1. Own and improve production forecasting systems for core financial metrics, especially current-quarter and longer-range revenue and bookings in a consumption-based business.
  2. Build and maintain scalable statistical and machine learning models that translate customer behavior, usage patterns, ramps, renewals, and business context into actionable forecasts.
  3. Design forecasting approaches that prioritize not only accuracy, but also stability, explainability, robustness, and operational trust.
  4. Establish and maintain high standards for model evaluation, backtesting, forecast decomposition, uncertainty quantification, and scenario analysis.
  5. Diagnose material forecast movements quickly and clearly, separating true business change from data issues, one-time events, timing shifts, and model artifacts.

Skills

Required

  • Python
  • SQL
  • statistical modeling
  • machine learning
  • forecasting
  • large-scale data systems
  • modern data platforms (Snowflake, BigQuery, Redshift, Spark)
  • systems thinking
  • monitoring
  • anomaly detection
  • validation
  • reproducibility
  • change management
  • communication skills
  • mentorship
  • technical leadership

Nice to have

  • consumption-based SaaS business model forecasting
  • executive-facing financial forecasts or planning systems
  • daily or near-daily production outputs

What the JD emphasized

  • production-grade statistical, forecasting, or machine learning systems
  • production rigor
  • operational trust
  • model evaluation
  • backtesting
  • forecast decomposition
  • uncertainty quantification
  • scenario analysis
  • material forecast movements
  • data issues
  • one-time events
  • timing shifts
  • model artifacts
  • reliability of the forecasting stack
  • monitoring
  • anomaly detection
  • validation checks
  • change management
  • reproducibility
  • lifecycle management
  • production processes
  • complex forecasting system
  • high-stakes outputs
  • responding quickly and effectively when something changes or breaks
  • systems thinking
  • monitoring
  • validation
  • anomaly detection
  • reproducibility
  • safe model or pipeline changes in production
  • daily or near-daily production outputs

Other signals

  • production forecasting systems
  • statistical and machine learning models
  • revenue forecasting
  • customer consumption behavior
  • workload ramps
  • renewals
  • leading indicators
  • financial planning processes
  • forecast accuracy
  • stability
  • operational trust
  • model evaluation
  • backtesting
  • forecast decomposition
  • uncertainty quantification
  • scenario analysis
  • material forecast movements
  • data issues
  • one-time events
  • timing shifts
  • model artifacts
  • reliability of the forecasting stack
  • monitoring
  • anomaly detection
  • validation checks
  • change management
  • reproducibility
  • lifecycle management
  • shared infrastructure
  • upstream dependencies
  • production processes
  • complex forecasting system
  • business drivers
  • high-quality business context
  • forecast quality
  • senior leaders
  • forecast changes
  • risks
  • model behavior
  • high-visibility
  • time-sensitive situations
  • technical rigor
  • production quality
  • decision-making
  • mentorship
  • technical leadership
  • consumption-based business model
  • executive-facing financial forecasts
  • planning systems
  • daily or near-daily production outputs