About the job
About Our Team
Join the User Operations team at OpenAI, where we are dedicated to delivering outstanding customer experiences with our innovative products. Our team tackles complex challenges, provides expert technical guidance, and empowers customers to fully leverage our solutions. Collaborating closely with Sales, Technical Success, Product, Engineering, and other departments, we ensure a seamless experience for our diverse clientele, ranging from emerging startups to established global firms. Given the rapid pace of innovation at OpenAI, transforming our extensive support data into real-time insights and scalable analytics is vital for maintaining exceptional customer satisfaction as we move toward AGI.
Role Overview
We are looking for a proactive Support Data Scientist who will analyze user-support data to identify trends, volume patterns, and friction points, converting insights into actionable strategies and continuous reporting. You will design and maintain self-serve dashboards that provide real-time information to stakeholders, ensuring seamless collaboration with Data Science and Engineering teams for clean data pipelines, robust modeling, and scalable tools. Your work will focus on proactive friction detection and real-time service health monitoring, delivering insights that drive informed decision-making rather than just aesthetic presentations.
This position is based in San Francisco, California, employing a hybrid work model with three days in the office each week. We also offer relocation assistance for new hires.
Key Responsibilities:
Analyze extensive support and product datasets to identify trends, key drivers, and user experience challenges, translating findings into actionable insights.
Develop and maintain user-friendly self-serve dashboards and reporting tools that empower non-technical teams to address their own data inquiries.
Create a standardized metrics framework for service performance, establishing automated data-sharing pipelines and scorecards with our BPO partners to maintain a unified real-time view of success.
Utilize large language models (LLMs) to build custom classifiers that automatically categorize and segment incoming data, enhancing routing accuracy, enriching self-service insights, and accelerating root-cause analysis.
Collaborate with Data Engineering to ensure reliable data pipelines, implement quality checks, and document processes for sustained excellence.

