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

OpenAI· Data Science
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San Francisco$441k - $515kFull-timePosted 4mo ago

About the role

Hands-on technical leadership role building AI/ML-powered intelligence products for GTM Intelligence and Technical Success, defining metrics and models to measure and improve customer adoption across OpenAI’s B2B products. Leads a small team, partners cross-functionally, and shapes the data science foundation and next-best-action systems for field teams.

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, helping identify at-risk customers and effective interventions.
  • Build, mentor, and lead a small team of data scientists and cross-functional analytics partners as the GTM Intelligence function scales.
  • Set technical standards for modeling, metrics, experimentation, documentation, and production readiness across the team's work.
  • Create team operating rhythms balancing urgent field needs with durable 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 opportunities, recommend playbooks, and explain evidence behind recommendations.
  • Partner with Technical Success leaders to enumerate playbooks, 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 in field workflows rather than 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 a high bar for analytical rigor, including causal evaluation, validation, data quality, and clear caveats.

Qualifications

  • 10+ years of experience in applied data science, analytics, machine learning, quantitative strategy, or closely related field.
  • Deep technical skill in SQL and Python, with ability to move from raw tables to production-quality models, metrics, and decision systems.
  • Strong 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 for distilling complex analysis into clear recommendations for technical partners, field teams, and executives.
  • Comfort partnering across technical and non-technical teams (Product, Engineering, Technical Success, Sales, RevOps, Finance, Data Engineering).
  • Track record of operating autonomously in fast-moving environments and raising data-driven decision quality.
  • 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 strong data science talent while creating a collaborative, high-accountability team culture.
  • Advanced degree in a quantitative field, or equivalent practical experience.

Skills

Required:SQLPythonStatistical modelingPropensity score modelingCommunication

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 additional paid office closures for focus and recharge, plus paid sick or safe time
  • Mental health and wellness support
  • Employer-paid basic life and disability coverage
  • Annual learning and development stipend
  • Daily meals in offices, and meal delivery credits as eligible
  • Relocation support for eligible employees
  • Additional taxable fringe benefits (e.g., charitable donation matching, wellness stipends)
  • Details available during hiring process
Last updated Jul 16, 2026. Interested? Apply on the company site.

About OpenAI

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