Data Analyst, User Operations

About the Team

The User Operations team is central to ensuring that our customers' experience with our products is nothing short of exceptional. We resolve complex issues, provide technical guidance, and support customers in maximizing value and adoption from deploying our products. We work closely with Sales, Technical Success, Product, Engineering and others to deliver the best possible experience to our customers at scale. OpenAI's customers represent a range of diverse backgrounds and maturity, from early-stage startups to established global enterprises. Given OpenAI’s breakneck shipping cadence and growth—and the expectation that it will only accelerate—transforming our rich support data into real‑time insights and scalable, self‑serve analytics is critical to sustaining exceptional customer experiences on the path to AGI.

About the Role

We’re seeking a User Operations Data Analyst who will dig deep into user‑support data—surfacing trends, volumes, and friction signals—and turn these findings into actionable insights and always‑on reporting. You’ll design, build, and maintain self‑serve dashboards that keep every stakeholder informed in real time, partnering closely with Data Science and Engineering to ensure clean pipelines, robust models, and scalable tooling. Think proactive friction detection and real‑time service‑health views that help us stay ahead of demand—delivering decision‑grade insights, not just prettier slide decks.

This role is based in San Francisco, California. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Explore large support and product datasets to uncover trends, volume drivers, and user‑experience pain points, distilling findings into clear, actionable narratives.

• Build, enhance, and maintain self‑serve dashboards and reporting tools, enabling non‑technical teams to answer their own data questions.

• Establish a unified metrics taxonomy for service‑health and performance—and build automated data‑sharing pipelines and scorecards with our BPO partners to ensure everyone operates from the same real‑time view of success

• Leverage LLMs to build bespoke classifiers that automatically label and segment inbound volumes—powering smarter routing, richer self‑serve insights, and swifter root‑cause analysis.

• Partner with Data Engineering to ensure reliable pipelines, implement data‑quality checks, and document sources of truth.

• Jump into high‑priority special projects to conduct bespoke deep‑dive analyses and deliver clear, strategic recommendations to leadership.

• Prototype quickly—leveraging ChatGPT, Jupyter notebooks, Retool, and other tools—to prove value before hardening with Engineering.

• Collaborate with Data Science on predictive models and experimentation, translating results into operational recommendations.

You might thrive in this role if you:

• 8 + years in analytics, business intelligence, or data science, ideally supporting customer support or operations teams.

• Expert‑level SQL skills and proficiency in Python or R for advanced analysis and automation

• Hands‑on experience designing and maintaining BI dashboards (e.g. Looker, Mode, Tableau, Sundial) with a focus on clarity and self‑serve usability.

• Hands‑on experience fine‑tuning or prompt‑engineering LLMs to build text classifiers, sentiment analysis, or tagging systems.

• Demonstrated ability to translate complex datasets into clear business stories and recommendations for both technical and non‑technical audiences.

• Familiarity with support metrics (SLAs, FCR, deflection) and ability to define service health KPIs.

• Strong cross‑functional communication skills—comfortable collaborating daily with engineers, data scientists, and operations leaders.

• An eye for detail, a zero‑defect mindset, and a bias toward action over theoretical perfection

• Skills & Tags

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