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

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 rigorous insight products that help leaders and field teams understand where customers are succeeding, where adoption is blocked, and what actions will accelerate durable growth.

We are building systems that make customer intelligence proactive: surfacing risk, expansion potential, product gaps, and repeatable playbooks before they show up as escalations or missed opportunities.

About the Role

As the Applied Data Science & Insights Lead for GTM Intelligence Solutions and Technical Success, you will be a hands-on technical leader responsible for shaping how OpenAI measures, understands, and improves customer adoption across our B2B products. You will build AI/ML-powered intelligence products that connect account health, product usage, customer lifecycle, support tier, qualitative sentiment, commercial context, and field actions into a practical operating system for GTM and Technical Success.

This role will build the data science foundation for Technical Success: defining the metrics, models, operating insights, and decision systems that help the team scale customer adoption and expansion with rigor.

You will also be expected to build and lead a small mighty team over time: setting direction, hiring and developing talent, creating operating cadences, and holding a high bar for technical rigor and business impact.

You will lead the development of models, metrics, and decision systems that recommend what GTM and Technical Success teams should do next, explain why, and measure whether those interventions worked. Your work will help customers move from pilots to production, deepen usage across products, identify high-value use cases, reduce churn risk, and create a faster feedback loop from the field back to Product and Research.

This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees.

The Vision

• Build a unified GTM intelligence layer that connects product telemetry, customer health, revenue, support tier, lifecycle stage, field activity, and qualitative feedback.

• Turn adoption breadth, usage depth, sentiment, and customer maturity signals into next-best-action systems for Technical Success and field teams.

• Create a measurement foundation for Technical Success playbooks, including whether recommended actions were taken and whether they improved customer outcomes.

• Help OpenAI understand customer happy paths: the use cases, product behaviors, and interventions that lead to durable adoption, expansion, and retention.

• Productize insights into workflows used by Technical Success, Sales, RevOps, Finance, Product, and executive leadership.

In This Role, You Will:

• 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 Technical Success and GTM identify which customers are most likely to adopt, which interventions can move adoption, and where support should focus.

• 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 that balance 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 account opportunities and risks, recommend playbooks, and explain the evidence behind each recommendation.

• 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 that are embedded into field workflows rather than living only as one-off analyses or static dashboards.

• Translate field feedback and account-level patterns into clear product and GTM recommendations for senior leadership.

• Collaborate with Data Engineering and RevOps to improve the 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.

• 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, including the ability to distill complex analysis into clear recommendations for technical partners, field teams, and executives.

• Comfort partnering across technical and non-technical teams, including Product, Engineering, Technical Success, Sales, RevOps, Finance, and Data Engineering.

• A track record of operating autonomously in fast-moving environments and raising the quality of 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 strong data science talent while creating a collaborative, high-accountability team culture.

• An advanced degree in a quantitative field, or equivalent practical experience.