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

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

Data Science - San Francisco

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The GTM Data Science team partners with Go-to-Market, Technical Success, Product, Engineering, Rev Ops, 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.

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.

• 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, Rev Ops, Finance, Product, and executive leadership.

• 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…