Data Scientist, GTM

Compensation

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this role is $290K - $340K per year, with generous equity, performance-related bonuses, and the following benefits:

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
• 401(k) retirement plan with employer match
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend to fuel your professional growth
• Daily meals in our offices, and meal delivery credits as eligible
• Relocation support for eligible employees
• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

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.

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.

Responsibilities

• 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.

Requirements

• 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.