About the job
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.
Responsibilities
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
Develop scalable 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
Qualifications
Minimum
No minimum qualifications listed.
Preferred
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