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OpenAI's GTM Data Science team is seeking a senior data scientist to own the analytical strategy for enterprise knowledge-worker adoption of ChatGPT Work, Codex, and connected enterprise systems. This is a hands-on, zero-to-one role focused on understanding how AI products become embedded in everyday work rather than being tried once.
You will define measurement frameworks for knowledge-worker adoption, including identity resolution, eligible populations, activation, retained usage, penetration, workflow depth, feature adoption, and monetization. You'll map the customer journey from initial exposure through first successful task, repeated workflows, multi-surface usage, and durable adoption. Your work will identify which personas, functions, use cases, product capabilities, and account conditions drive deep and retained usage.
Key responsibilities include designing and evaluating experiments across onboarding, enablement, workflow templates, connectors, pilots, and product launches. You'll combine behavioral data with customer and field evidence to translate findings into crisp recommendations for Product, GTM, Finance, and executive audiences. You'll operationalize successful work through durable datasets, scorecards, recurring business narratives, and decision cadences while partnering with Analytics Engineering and product teams to improve instrumentation and data quality.
You should enjoy creating clarity from ambiguous problems without stable definitions or clean datasets. You move comfortably between SQL, Python, metric design, experimentation, customer evidence, strategy, and executive communication. You think in terms of user journeys and behavioral mechanisms, not just dashboards. You can distinguish product usage from durable customer value and challenge attractive narratives when evidence doesn't support them.
Required qualifications include significant experience in data science, product analytics, growth analytics, economics, statistics, or related quantitative fields. You need strong hands-on ability in SQL and Python with experience working with large, imperfect behavioral datasets. You should have experience defining activation, retention, engagement, funnel, or product-adoption metrics, with strong knowledge of experimentation, causal inference, cohort analysis, and segmentation. You must translate ambiguous business questions into structured analyses, independently define analytical direction, and influence senior stakeholders through clear tradeoff framing. Strong written and verbal communication skills are essential.
Preferred experience includes enterprise SaaS, collaboration products, AI products, developer tools, productivity software, or multi-product platforms. Experience connecting product usage to revenue, expansion, customer health, enablement, or GTM interventions is valuable. Background in identity resolution, cross-surface journeys, telemetry design, evolving product taxonomies, or qualitative customer evidence is a plus. Experience establishing new data science areas, operating models, technical roadmaps, or recurring executive decision cadences is preferred.
