Data Scientist, GTM & Enterprise AI Acceleration

About the Team OpenAI’s GTM Data Science team helps shape how our products are adopted, monetized, and scaled across organizations. We work at the intersection of Product, Go-to-Market, Finance, Research, and Data, turning product usage, customer evidence, and market signals into decisions that grow durable enterprise value. We’re looking for a senior Data Scientist to own the analytical strategy for enterprise knowledge-worker adoption. As ChatGPT Work and Codex become capable of research, analysis, document creation, spreadsheets, presentations, internal knowledge synthesis, and other agentic workflows, you will help determine how these products become embedded in everyday work—not merely tried once. About the Role You will define how we measure activation, retained usage, workflow depth, and value across ChatGPT Work, Codex, and connected enterprise systems. You will explain why adoption succeeds or stalls and identify the product, enablement, and commercial interventions most likely to create durable usage. This is a hands-on, zero-to-one role. You will work through imperfect telemetry, overlapping product surfaces, evolving definitions, and ambiguous business questions. You will partner closely with GTM, Product, Finance, Research, Customer Deployment, Analytics Engineering, and Data Science. Your work is successful when it changes a product, GTM, or investment decision. In This Role, You Will - Define a trusted measurement framework for knowledge-worker adoption, including identity, eligible populations, activation, retained usage, penetration, workflow depth, feature adoption, and monetization. - Map the knowledge-worker journey from initial exposure through first successful task, repeated workflows, multi-surface usage, and durable adoption. - Identify which personas, functions, use cases, product capabilities, and account conditions are associated with deep and retained usage. - Design and evaluate experiments and quasi-experiments across onboarding, enablement, workflow templates, connectors, pilots, customer deployment support, and product launches. - Combine behavioral data with customer and field evidence, then translate the findings into crisp recommendations for Product, GTM, Finance, and executive audiences. - 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 Might Thrive in This Role If You - Enjoy creating clarity from problems that do not yet have stable definitions, clean datasets, or a settled playbook. - Move comfortably between SQL, Python, metric design, experimentation, customer evidence, strategy, and executive communication. - Think in terms of user journeys and behavioral mechanisms—not only dashboards and aggregate metrics. - Can distinguish product usage from durable customer value and simulated value from realized business outcomes. - Proactively align stakeholders, document decisions, and surface data-quality risks before they affect important decisions. - Are comfortable challenging an attractive narrative when the evidence does not support it. Qualifications - Significant experience in data science, product analytics, growth analytics, economics, statistics, or a related quantitative field. - Strong hands-on ability in SQL and Python, including experience working with large and imperfect behavioral datasets. - Experience defining activation, retention, engagement, funnel, or product-adoption metrics, with strong knowledge of experimentation, causal inference, cohort analysis, and segmentation. - Ability to translate ambiguous business questions into structured analyses, independently define the analytical direction, and bring senior stakeholders along through clear tradeoff framing. - Strong written and verbal communication skills, including a demonstrated ability to influence senior technical and non-technical stakeholders. Preferred Qualifications - Experience with 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 other GTM interventions. - Experience with identity resolution, cross-surface journeys, telemetry design, evolving product taxonomies, or qualitative customer evidence. - Experience helping establish a new data science area, operating model, technical roadmap, or recurring executive decision cadence. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we mu

Significant experience in data science, product analytics, growth analytics, economics, statistics, or a related quantitative field. Strong hands-on ability in SQL and Python, including experience working with large and imperfect behavioral datasets. Experience defining activation, retention, engagement, funnel, or product-adoption metrics, with strong knowledge of experimentation, causal inference, cohort analysis, and segmentation. Ability to translate ambiguous business questions into structured analyses, independently define the analytical direction, and bring senior stakeholders along through clear tradeoff framing. Strong written and verbal communication skills, including a demonstrated ability to influence senior technical and non-technical stakeholders.

Define a trusted measurement framework for knowledge-worker adoption, including identity, eligible populations, activation, retained usage, penetration, workflow depth, feature adoption, and monetization. Map the knowledge-worker journey from initial exposure through first successful task, repeated workflows, multi-surface usage, and durable adoption. Identify which personas, functions, use cases, product capabilities, and account conditions are associated with deep and retained usage. Design and evaluate experiments and quasi-experiments across onboarding, enablement, workflow templates, connectors, pilots, customer deployment support, and product launches. Combine behavioral data with customer and field evidence, then translate the findings into crisp recommendations for Product, GTM, Finance, and executive audiences. 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.