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# Data Scientist, Inference Capacity Optimization

- **Company:** OpenAI Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $293,000.0 - $325,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Systems Engineering, Big Data, Distributed Systems, Python (Programming Language), Machine Learning, SQL Databases, AI Infrastructure, Reinforcement Learning, Information Technology, Data Analytics
- **Published:** August 2, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/17792560?backUrl=%2Fcareer%2F17792560%2FData-Scientist-Inference-Capacity-Optimization-California-San-Francisco

## About the Role

* 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.

Preferred Skills

 * Capacity planning
 * Distributed systems
 * AI infrastructure
 * Datacenter design and buildout
 * Queueing theory
 * Time-series forecasting
 * Operations research
 * Supply-demand modeling
 * Reinforcement learning for resource allocation
 * Cost optimization

## Description

OpenAI's Industrial Compute organization is responsible for ensuring our compute infrastructure scales efficiently to support millions of users and increasingly sophisticated AI models., We're looking for a Data Scientist to partner closely with Capacity Systems Engineering, Infrastructure, Product, and Research to optimize inference capacity across our global GPU fleet. This role combines statistical modeling, large-scale data analysis, forecasting, and systems thinking to drive critical decisions around infrastructure investments, performance-efficiency trade-offs, and customer experience., * 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.


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