Data Scientist, Core Experimentation

About the Team The Statsig team at OpenAI https://openai.com?utm_source=chatgpt.com builds and operates the experimentation platform that powers product development, measurement, and decision-making across the company. We partner closely with product, engineering, and infrastructure teams to ensure experiments are trustworthy, statistically rigorous, and scalable to the needs of frontier AI products. Our mission is to help teams make better decisions through reliable experimentation. We care deeply about statistical correctness, pragmatic solutions, and building systems that researchers and engineers can trust at massive scale. The team operates at the intersection of experimentation methodology, data infrastructure, causal inference, and product analytics. We are looking for experienced experimentation experts who want to shape the future of experimentation in the AI era. About the Role We are hiring a Staff-level Data Scientist to help lead the evolution of OpenAI’s core experimentation platform. This role is focused on improving the statistical rigor, reliability, and practical usability of experimentation across the company. You’ll work on some of the hardest problems in online experimentation: sample ratio mismatch detection, variance reduction, bias mitigation, metric design, triggered analysis, heterogeneous treatment effects, sequential testing, and experimentation in complex ML systems. You’ll also help translate advanced statistical concepts into pragmatic systems and product experiences that teams can actually use. This is a highly technical individual contributor role with significant influence across methodology, platform architecture, and experimentation best practices. The ideal candidate combines deep statistical expertise with strong systems intuition and hands-on experience building or operating experimentation platforms at scale. In this role, you will: - Drive the statistical direction and technical strategy for OpenAI’s experimentation platform - Design and improve experimentation methodologies used across product and research teams - Build pragmatic solutions to real-world experimentation challenges, balancing rigor with operational simplicity - Improve the reliability and trustworthiness of experiment results, including detection and prevention of bias, logging issues, and data quality failures - Developscalable analytical systems and pipelines in Python and distributed compute environments - Partner with engineers and product teams to improve experiment design, metric quality, and decision-making practices - Lead investigations into complex experimentation anomalies and measurement failures - Establish best practices for experimentation governance, interpretation, and statistical correctness - Mentor other data scientists and raising the overall technical bar for experimentation and causal inference You might thrive in this role if you have: - Experience building, scaling, or operating experimentation platforms at a large technology company - Deep expertise in statistics, causal inference, and online experimentation methodology - Strong understanding of practical experimentation challenges in production systems - Experience with areas such as variance reduction, CUPED, sequential testing, SRM detection, metric design, or heterogeneous effects - Strong coding and systems skills in Python and large-scale data processing frameworks (e.g. Spark) - Experience designing analytical data models and scalable experimentation pipelines - Ability to communicate complex statistical concepts clearly to technical and non-technical audiences - Track record of influencing technical strategy through hands-on technical leadership - Experience in large-scale product experimentation, ML experimentation, ranking systems, marketplace systems, or similar high-scale experimentation domains is highly valued Workplace & Location This role is based in Bellevue. We use a hybrid work model and value in-person collaboration for techn