Applied Data Science & Insights Leader - GTM Intelligence Solutions and Technical Success

Details

The GTM Data Science team partners with Go-to-Market, Technical Success, Product, Engineering, RevOps, and Strategic Finance to build the shared intelligence layer for OpenAI's B2B business. The team turns product usage, customer behavior, revenue, field activity, and customer feedback into insight products that help leaders and field teams understand customer success, adoption blockers, and actions that accelerate durable growth.

The role is responsible for shaping how OpenAI measures, understands, and improves customer adoption across its B2B products. It involves building AI/ML-powered intelligence products that connect account health, product usage, customer lifecycle, support tier, qualitative sentiment, commercial context, and field actions into an operating system for GTM and Technical Success. The role will also build and lead a small data science team over time.

This role is based in San Francisco, California. OpenAI uses a hybrid work model of three days in the office per week and offers relocation assistance to new employees.

Responsibilities

• Define and lead the roadmap for GTM Intelligence and Technical Success insight products in partnership with cross-functional leaders.
• Build the data science foundation for Technical Success, including core metrics, customer health definitions, intervention measurement, and reusable playbook analytics.
• Develop propensity score models for model and product feature adoption.
• Build, mentor, and lead a team of data scientists and cross-functional analytics partners.
• Set technical standards for modeling, metrics, experimentation, documentation, and production readiness.
• Create operating rhythms that balance urgent field needs with roadmap execution, quality review, and stakeholder alignment.
• Build predictive and causal models for customer health, expansion propensity, churn risk, adoption depth, use-case fit, and intervention effectiveness.
• Design next-best-action systems that identify account opportunities and risks, recommend playbooks, and explain the evidence behind recommendations.
• Partner with Technical Success leaders to enumerate playbooks and actions, instrument action tracking, and measure outcomes over time.
• Develop customer segmentation and benchmarking frameworks across products, industries, personas, support tiers, and lifecycle stages.
• Create scalable insight products embedded into field workflows rather than limited to one-off analyses or static dashboards.
• Translate field feedback and account-level patterns into product and GTM recommendations for senior leadership.
• Collaborate with Data Engineering and RevOps to improve data foundations connecting product telemetry, Salesforce, support signals, revenue, and qualitative feedback.
• Maintain analytical rigor through causal evaluation, validation, data quality, and clear caveats.

Requirements

• 10+ years of experience in applied data science, analytics, machine learning, quantitative strategy, or a closely related field.
• Deep technical skill in SQL and Python, with the ability to move from raw tables to production-quality models, metrics, and decision systems.
• Strong applied experience with statistical modeling, causal inference, machine learning, customer segmentation, churn or health modeling, or recommendation systems.
• Experience with propensity score modeling, uplift modeling, or related causal methods for adoption, activation, retention, or product feature usage.
• Experience building production or workflow-embedded data products for GTM, sales, customer success, technical success, growth, or enterprise SaaS teams.
• Product intuition and business judgment for turning ambiguous questions into repeatable models, tools, metrics, and operating cadences.
• Excellent communication skills and the ability to distill complex analysis into clear recommendations for technical partners, field teams, and executives.
• Experience partnering across Product, Engineering, Technical Success, Sales, RevOps, Finance, and Data Engineering.
• A track record of operating autonomously in fast-moving environments and improving how teams use data to make decisions.
• Experience leading teams or serving as a technical lead for multi-person data science initiatives, including mentoring, roadmap-setting, and quality review.
• Ability to hire, develop, and retain data science talent while creating a collaborative, high-accountability team culture.
• An advanced degree in a quantitative field or equivalent practical experience.

Benefits

• Equity, performance-related bonuses for eligible employees, and comprehensive benefits.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Pre-tax accounts for health, dependent care, and commuter expenses.
• 401(k) retirement plan with employer match.
• Paid parental, medical, and caregiver leave.
• Paid time off, company holidays, and office closures.
• Mental health and wellness support.
• Employer-paid basic life and disability coverage.
• Annual learning and development stipend.
• Daily meals in offices and eligible meal delivery credits.
• Relocation support for eligible employees.
• Additional benefits may include charitable donation matching and wellness stipends.

OpenAI is an equal opportunity employer committed to reasonable accommodations and compliance with applicable employment laws.