Data Scientist, CoreWeave

• Bachelor’s or Master’s degree in Operations Research, Statistics, Economics, Computer Science, or a related quantitative field
• 3-5 years of experience in demand forecasting, capacity planning, or applied predictive modeling, including hands-on ownership of forecasting models or workflows
• Proficiency in Python and SQL for data manipulation, analysis, and predictive modeling
• Experience writing production-quality, version-controlled code
• Working exposure to S&OP, IBP, or other consensus-based planning environments
• Experience presenting forecast outputs, utilization analyses, or planning insights to cross-functional stakeholders
• Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren’t a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk

Desirable

• CoreWeave is seeking a Data Scientist 2 - Forecasting to build and improve the forecasting models and pipelines that power capacity planning. You will own defined forecasting workstreams across day-ahead through quarterly horizons, helping the business understand utilization patterns, demand signals, and forecast variance
• You will contribute analysis and automated data flows that feed the consensus planning process
• In this role, you’ll partner closely with Capacity, Sales, Product, Finance, and Data Center Operations to help translate demand signals into infrastructure decisions
• Build, tune, and maintain forecasting models for GPU, CPU, and Storage assets across defined planning horizons
• Prepare utilization and demand analyses that support the consensus planning cycle and help synthesize inputs from Sales and Product
• Partner with Capacity Managers, Finance, and Data Center Operations to validate model assumptions against physical constraints and financial targets
• Develop models and analyses that interpret utilization data, surface consumption patterns, explain forecast variance, and recommend improvements to models or source data
• Build and maintain production-grade pipeline components that automate recurring forecast refreshes and reduce manual intervention