About
OpenAIData Scientist, User Operations
Description
OpenAI’s User Operations organization is building the data and intelligence layer behind AI-assisted operations — the systems that decide when automation should help users, when humans should step in, and how both improve over time. Our flagship platform is transforming customer support into a model for “agent-first” operations across OpenAI.
About the Role
As a Data Scientist on User Operations, you’ll design the models, metrics, and experimentation frameworks that power OpenAI’s human-AI collaboration loop. You’ll build systems that measure quality, optimize automation, and turn operational data into insights that improve product and user experience at scale. You’ll partner closely with Support Automation Engineering, Product, and Data Engineering to ensure our data systems are production-grade, trusted, and impactful.
This role is based in San Francisco or New York City. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees.
Why it matters
Every conversation users have with OpenAI products produces signals about how humans and AI interact. User Ops Data Science turns those signals into insights that shape how we support users today and design agentic systems for tomorrow. This is a unique opportunity to help define how AI collaboration at scale is measured and improved inside OpenAI.
In this role, you will:
Build and own metrics, classifiers, and data pipelines that determine automation eligibility, effectiveness, and guardrails.
Design and evaluate experiments that quantify the impact of automation and AI systems on user outcomes like resolution quality and satisfaction.
Develop predictive and statistical models that improve how OpenAI’s support systems automate, measure, and learn from user interactions.
Partner with engineering and product teams to create feedback loops that continuously improve our AI agents and knowledge systems.
Translate complex data into clear, actionable insights for leadership and cross-functional stakeholders.
Develop and socialize dashboards, applications, and other ways of enabling the team and company to answer product data questions in a self-serve way
Contribute to establishing data science standards and best practices in an AI-native operations environment.
Partner with other data scientists across the company to share knowledge and continually synthesize learnings across the organization
Qualifications
10+ years of experience in data science roles within product or technology organizations.
Expertise in statistics and causal inference, applied in both experimentation and observational causal inference studies.
Expert-level SQL and proficiency in Python for analytics, modeling, and experimentation.
Proven experience designing and interpreting experiments and making statistically sound recommendations.
Experience building data systems or pipelines that power production workflows or ML-based decisioning.
Experience developing and extracting insights from business intelligence tools, such as Mode, Tableau, and Looker.
Strategic and impact-driven mindset, capable of translating complex business problems into actionable frameworks.
Ability to build relationships with diverse stakeholders and cultivate strong partnerships.
Strong communication skills, including the ability to bridge technical and non-technical stakeholders and collaborate across various functions to ensure business impact.
Ability to operate effectively in a fast-moving, ambiguous environment with limited structure.
Strong communication skills and the ability to translate complex data into stories for non-technical partners.
Nice-to-haves:
Familiarity with large language models or AI-assisted operations platforms.
Experience in operational automation or customer support analytics.
Background in experimentation infrastructure or human-AI interaction systems.