Founding Data Scientist, Capacity & Pricing Optimization

The Role

GPUs are the most expensive and supply-constrained input in AI. Our ability to effectively deploy them towards our customer demand is one of the critical inputs to our growth. We're hiring our first Data Scientist to build and own the quantitative models that drive pricing, packaging, and capacity allocation in order to optimize capacity and balance supply with demand.

The ideal candidate brings expertise at the intersection of operations research and economics with the ability to both model our market, build dynamic pricing models, and design experiments and causal studies. This is a hands-on role: you'll write production-quality code, ship models that drive real decisions, and bring an true ownership mentality to help set priorities in addition to executing on them.

Minimum Qualifications

• Master’s degree in Operations Research, Economics, Industrial Engineering, Statistics, or a related quantitative field with 7+ years of hands-on experience in pricing, revenue management, or optimization. PhD preferred.

• Strong grounding in at least two of: stochastic optimization, revenue management and pricing, causal inference and experimental design, or combinatorial optimization.

• A track record of delivering production quality models that drive direct business impact.

• Strong coding skills and experience with optimization tooling & methodologies.

• Ability to communicate tradeoffs and uncertainty clearly to engineers and executives.

Preferred Qualifications

• Experience on capacity planning, forecasting, or pricing science teams at a cloud computing provider or marketplace business.

• Familiarity with GPU infrastructure and LLM inference economics (throughput, batching, utilization, cost per token).

• Experience as the first quantitative hire in a new function, building models and data pipelines from scratch.