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OpenAI's Industrial Compute organization is seeking a Data Scientist to optimize inference capacity across its global GPU fleet. This role bridges statistical modeling, large-scale data analysis, forecasting, and systems thinking to drive infrastructure investment decisions and performance-efficiency trade-offs.
You will transform operational data into actionable insights that directly influence how OpenAI allocates and scales one of the world's largest AI compute environments. Key responsibilities include building statistical and machine learning models to profile and improve GPU utilization, latency, throughput, and fleet efficiency; developing forecasting models for inference demand across products, regions, and model families; analyzing production workloads to identify bottlenecks and optimization opportunities; and partnering with Capacity Systems Engineering to inform infrastructure planning and long-term GPU investment strategies.
Additional responsibilities include designing experiments and simulations to evaluate scheduling policies and serving strategies, building dashboards and operational metrics for leadership decision-making, and collaborating with Product, Research, Finance, and Infrastructure teams to align compute planning with business growth and model roadmaps. You will communicate technical findings clearly to both engineering teams and executive leadership.
Required qualifications include an MS or PhD in Statistics, Computer Science, Operations Research, Applied Mathematics, Economics, or related quantitative discipline (or equivalent industry experience); 5+ years in infrastructure data science; strong Python and SQL expertise; experience building forecasting, optimization, or predictive models; strong understanding of experimentation, statistical inference, and causal analysis; and ability to communicate analytical insights to executive stakeholders.
Preferred skills include capacity planning, distributed systems, AI infrastructure, datacenter design, queueing theory, time-series forecasting, operations research, supply-demand modeling, reinforcement learning for resource allocation, and cost optimization.
