Data Scientist optimizing inference capacity on OpenAI's global GPU fleet. Build statistical/ML forecasting models, analyze workloads for bottlenecks, design experiments for scheduling/serving tradeoffs, and partner with engineering to guide infrastructure investments and efficiency improvements. Requires MS/PhD and 5+ years in infrastructure data science.
293k – 325k/yr
Hybrid5+ YOEData Science
About the role
Key Responsibilities
Build statistical and machine learning models to profile and improve GPU utilization, latency, throughput, and overall fleet efficiency.
Develop forecasting models for inference demand across products, regions, and model families.
Analyze production workloads to identify latency bottlenecks and capacity constraints, highlighting optimization opportunities.
Partner with Capacity Systems Engineering to inform infrastructure planning and long-term GPU investment strategies.
Design experiments and simulations to evaluate scheduling policies, serving strategies, and infrastructure tradeoffs.
Build dashboards and operational metrics that enable leadership to make data-driven capacity decisions.
Collaborate with Product, Research, Finance, and Infrastructure teams to align compute planning with business growth and model roadmaps.
Communicate technical findings clearly to both engineering teams and executive leadership.
Qualifications
MS or PhD in Statistics, Computer Science, Operations Research, Applied Mathematics, Economics, or related quantitative discipline (or equivalent industry experience).
5+ years of experience working in the infrastructure data science space.
Strong expertise in Python and SQL.
Experience building forecasting, optimization, or predictive models.
Strong understanding of experimentation, statistical inference, and causal analysis.
Experience communicating analytical insights to executive stakeholders.
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