Senior Data Scientist

Snowflake•Menlo Park, CA

About The Position

Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level. About the Team The Finance Data Science team owns the forecasting systems that power Snowflake’s revenue planning and long-term financial strategy. Our work supports corporate planning, executive decision-making, and investor reporting, and we partner closely with Product and Sales to understand customer behavior and product impact. We operate at the intersection of machine learning, statistical research, and corporate finance, building production-grade forecasting infrastructure that is foundational to how the company plans and operates. The Role As a Senior Data Scientist, you will independently lead high-impact modeling initiatives and build production-ready forecasting systems for core financial metrics. You will work on complex, open-ended problems at the intersection of machine learning and business strategy, translating real-world financial questions into rigorous, scalable models.

Requirements

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.

Responsibilities

Design and implement advanced time-series and probabilistic models (e.g., hierarchical models, state-space models, Bayesian approaches, multivariate forecasting).
Contribute to internal tooling and shared infrastructure that enables scalable forecasting and analytics.
Establish best practices for model evaluation, backtesting, uncertainty quantification, and scenario simulation.
Apply advanced statistical and ML techniques to model customer behavior, product adoption, revenue dynamics, and cost trends.
Drive improvements in automation, monitoring, drift detection, and lifecycle management of forecasting models.
Partner with Finance, Product, and Sales teams to quantify the impact of new initiatives and understand key business drivers.
Mentor and provide technical guidance to other data scientists; raise the bar for modeling rigor and production quality across the team.