Founding Data Scientist, Capacity & Pricing Optimization
San Mateo, CA · HybridFull-time$250–300K/yrPosted 5 days agoStill listed 4 days ago
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At a glance
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Requirements
Credentials this posting asks for.
Job overview
Fireworks is hiring its first Data Scientist to build and own quantitative models for pricing, packaging, and capacity allocation. The role focuses on balancing GPU supply with customer demand through supply-demand modeling, pricing optimization, experiments, and GPU mix optimization. The candidate will write production-quality code, ship models that inform business decisions, and help set priorities.
Skills & qualifications
Skills
Qualifications
Full job description
About Us: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. 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.
What You'll Work On:
Supply/Demand Modeling
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Build fill-rate models for serverless inference, balancing utilization against performance and latency targets.
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Model how traffic is shared and multiplexed across models and customers to set safe capacity buffers.
Pricing Optimization
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Design pricing for low-utilization capacity, including spot and preemptible offerings, off-peak and overnight windows, and sub-scale capacity pools.
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Develop pricing policies to optimize our capacity utilization and price-yield.
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Design execute experiments and causal studies to estimate elasticity across customer segments, models, and products.
GPU Mix Optimization
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Optimize the model-hardware matching across GPU generations, vendors, clouds, and regions, accounting for workload fit, substitution, and lead times.
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Optimize our capacity across commitment types and terms, quantifying the tradeoff between cost, flexibility, and utilization risk to guide how we source capacity.
What we’re looking for:
Minimum Qualifications
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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.
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Strong grounding in at least two of: stochastic optimization, revenue management and pricing, causal inference and experimental design, or combinatorial optimization.
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A track record of delivering production quality models that drive direct business impact.
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Strong coding skills and experience with optimization tooling & methodologies.
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Ability to communicate tradeoffs and uncertainty clearly to engineers and executives.
Preferred Qualifications
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Experience on capacity planning, forecasting, or pricing science teams at a cloud computing provider or marketplace business.
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Familiarity with GPU infrastructure and LLM inference economics (throughput, batching, utilization, cost per token).
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Experience as the first quantitative hire in a new function, building models and data pipelines from scratch.
Why Fireworks?
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Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
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Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
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Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
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Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
How this job compares
Set against the 4,830 open Data Scientist jobs on Olive Jobs, counted November 3, 2026.
- Pay
- This posting$250–300K/yr
- California median$156–236K/yr671 salary ranges
- US median$135–213K/yr3,705 salary ranges
This range's midpoint, $275K, is 38% above the California median midpoint ($200K) — in the top quarter of Data Scientist salary ranges in California.
- Openings
- Olive Jobs lists 809 open Data Scientist jobs in California and 4,830 across the US. 6% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 7 years of experience — more than three in four Data Scientist postings that state a minimum ask for (median 5 years, across 4,140 postings).
- Education
- This posting asks for a master's degree; 617 of the 4,830 open Data Scientist jobs (13%) ask for a master's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 3, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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