Senior Data Scientist

The Finance Data Science team owns the forecasting systems that power Snowflake's financial planning, operating cadence, and long-term strategy. Our work informs executive decision-making, corporate planning, investor reporting, and cross-functional decisions across Finance, Sales, and Product.

We build and operate production forecasting systems for Snowflake's core money-in metrics, with a particular focus on revenue and bookings in a consumption-based business. Our forecasts are highly visible, widely used, and foundational to how the company plans and operates. This is a high-trust team operating at the intersection of statistical modeling, production systems, and financial decision-making.

We are hiring a Senior Applied Scientist to own and advance mission-critical forecasting systems used across the company. This role is not just about building models. It is about developing reliable, explainable, production-grade forecasting systems that leaders can trust to make decisions.

You will work on high-impact, open-ended problems involving revenue forecasting, customer consumption behavior, workload ramps, renewals, and other leading indicators that feed Snowflake's broader financial planning processes. You will partner closely with Finance, Sales, Product, and Analytics Engineering to improve forecast accuracy, stability, and operational trust.

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

- Own and improve production forecasting systems for core financial metrics, especially current-quarter and longer-range revenue and bookings in a consumption-based business.

- Build and maintain scalable statistical and machine learning models that translate customer behavior, usage patterns, ramps, renewals, and business context into actionable forecasts.

- Design forecasting approaches that prioritize not only accuracy, but also stability, explainability, robustness, and operational trust.

- Establish and maintain high standards for model evaluation, backtesting, forecast decomposition, uncertainty quantification, and scenario analysis.

- Diagnose material forecast movements quickly and clearly, separating true business change from data issues, one-time events, timing shifts, and model artifacts.

- Improve the reliability of the forecasting stack through better monitoring, anomaly detection, validation checks, change management, reproducibility, and lifecycle management.

- Partner closely with Analytics Engineering and peer Data Scientists on shared infrastructure, upstream dependencies, and production processes across a complex forecasting system.

- Work cross-functionally with Finance, Sales and Product to understand business drivers, incorporate high-quality business context, and improve forecast quality.

- Communicate clearly with senior leaders on forecast changes, risks, and model behavior, especially in high-visibility or time-sensitive situations.

- Raise the bar for technical rigor, production quality, and decision-making across the team through mentorship and technical leadership.

- 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, or machine learning systems with meaningful business impact.

- Strong hands-on experience with forecasting problems, ideally in revenue, demand, supply, capacity, consumption, or other business-critical planning contexts.

- Deep modeling skills, including strong judgment around when to use simpler driver-based approaches versus more advanced methods such as hierarchical, Bayesian, probabilistic, deep learning, or state-space models.

- Strong proficiency in Python and SQL, with the ability to manipulate data, build models, and productionize analyses efficiently.

- Experience working with large-scale data systems and modern data platforms such as Snowflake, BigQuery, Redshift, or Spark.

- Demonstrated ownership of high-stakes outputs used by business or executive stakeholders, including experience responding quickly and effectively when something changes or breaks.

- Strong systems thinking, including experience with monitoring, validation, anomaly detection, reproducibility, and safe model or pipeline changes in production.

- Excellent communication skills, including the ability to explain complex forecast movements, uncertainty, and tradeoffs to senior business stakeholders.

- A track record of leading through ambiguity, influencing cross-functional partners, and elevating technical standards across a team.

- Forecasting in a consumption-based, usage-based, or hybrid SaaS business model.

- Experience with executive-facing financial forecasts or planning systems.

- Experience owning models or data products with daily or near-daily production outputs.

- Experience operating in environments where reliability, trust, and fast issue response matter as much as raw model performance.

- Experience mentoring other scientists and helping shape shared modeling or production standards.

In this role, success means you can build and operate forecasts that are not only technically strong, but trusted, stable, and decision-useful. You know how to balance modeling sophistication with business practicality. You can move quickly when forecasts change, communicate clearly about why they changed, and improve the system over time so that it becomes more reliable and more trusted.