Data Scientist, ChatGPT for Work

OpenAI’s mission is to ensure AI benefits all of humanity. ChatGPT for Work supports that mission by helping more people access real leverage from AI in their day-to-day jobs—so they can spend less time on busywork and coordination, and more time on the work that’s meaningful and additive. We’re building an AI-native workspace where AI acts as a superassistant for everyday tasks and a coworker you can hand work off to—then review, edit, and approve with confidence.

And we’re doing it in a way organizations can trust, by grounding experiences in the right company context and systems safely and reliably.

About the Role

As the Data Scientist for ChatGPT for Work, you’ll shape product strategy through data: uncover the user problems most worth solving, form sharp hypotheses about what will move team and business outcomes, and influence what we build next by presenting compelling recommendations grounded in rigorous evidence. You’ll be the DRI for the Work insight → strategy → experiment → decision loop—defining what “success” means for teams, pinpointing the highest-leverage adoption and retention bottlenecks, and turning signals into clear product direction.

You’ll partner closely with Product, Engineering, Research, and Finance to ensure our metrics are trusted, our experimentation is rigorous, and our insights turn into shipped improvements.

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

In this role, you will:

• Own the core KPI framework for ChatGPT for Work, spanning onboarding, activation, engagement, retention, and expansion, as well as quality/trust guardrails.

• Build end-to-end funnels that identify where individuals and teams succeed or get stuck, from first workspace setup through repeat usage and long-term team adoption and value creation.

• Define and operationalize “time-to-value” and collaboration loop metrics, and connect them to business outcomes.

• Design and evaluate experiments and rollouts to quantify the impact of product changes across key Work surfaces and flows.

• Partner with product and engineering teams to improve instrumentation, data quality, and metric definitions so decisions are fast and correct.

• Translate complex analysis into clear, compelling insights that shape product strategy and roadmap decisions.

• Help establish data science standards and best practices for measuring human–AI collaboration and AI-native work outcomes.

• Partner with other data scientists across the company to share learnings and raise the bar on measurement, experimentation, and decision-making.

• 10+ years in data science / analytics in in high-velocity product environments

• Direct experience working on B2B products (SaaS, collaboration/workspace, developer tools, or enterprise)

• Expert SQL + strong Python

• Strong experimentation + causal inference judgment (incl. when clean A/B tests aren’t feasible)

• Strong product sense/taste: can turn messy signals into crisp hypotheses and roadmap direction

• Proven ability to inspire and influence PM/Eng/Design + leadership through data storytelling

• Autonomous operator who sets the insights/measurement agenda

• Excellent executive communication; thrives in ambiguous, fast-moving environments

• AI-native operator (non-negotiable): “super AI-pilled”—first to adopt new AI tools, uses them daily to increase throughput, and turns them into durable org workflows

Nice-to-haves

• Experience with agentic and/or AI-native B2B products (agents, copilots, workflow automation, AI collaboration)

• Experience measuring AI product quality, trust, and human-AI interaction signals

• Familiarity with enterprise admin/security constraints and how they shape adoption

• Experience with B2B PLG growth loops and monetization/seat expansion dynamics

Why it matters

Work is one of the biggest places AI can improve people’s lives. If we get ChatGPT for Work right, we help individuals and teams spend less time on busywork and coordination and more time on the work that’s meaningful and additive—making better decisions, moving faster, and producing higher-quality outcomes. This role matters because the way we measure, learn, and iterate will determine whether AI becomes a genuinely trusted superassistant and coworker for everyday knowledge work, at scale.