Data Scientist, GTM Growth Products

ABOUT THE ROLE We are hiring a Data Scientist to support GTM Growth Products, a cross-functional team building the internal systems that turn OpenAI's technology into measurable revenue leverage. Our mandate is to prove what an AI-native revenue organization can become: one where every signal, workflow, and customer moment can be understood, acted on, and improved by agents and humans together. We build systems that help GTM scale faster than headcount by finding opportunity, automating low-complexity work, routing judgment-heavy work to the right human, and learning from every outcome. The team is focused on live GTM workflows across agentic sales, prospecting, routing, workflows, sales policy operations, measurement, attribution, and continuous learning loops. In this role, you will be embedded with Product, Engineering, and GTM partners to ensure these systems drive measurable business outcomes: ARR, pipeline, conversion, automation, and operating efficiency. WHAT YOU’LL DO - Define north-star, leading, and guardrail metrics for agentic revenue systems, including agentic ARR, incremental pipeline, conversion lift, automation rate, response time, hours saved, and operating efficiency. - Design measurement and experimentation frameworks for always-on GTM systems, using holdouts, staged rollouts, quasi-experimental methods, and launch-specific decision criteria where traditional A/B testing is not enough. - Partner with PMs and engineers to instrument, evaluate, and monitor launches so every meaningful release has observability and a credible read on incremental value. - Translate messy account, person, product, behavioral, and model-driven signals into decisions about what to automate, what to route, where humans should intervene, and what the system should learn next. - Build repeatable decision loops from pre-launch criteria to post-launch readout to shipped product, policy, or operational changes. - Develop dashboards, readouts, and evaluation systems that help GTM leaders, DS, PM, and engineering teams understand where automation is creating leverage and where quality, policy, or workflow design needs to improve. - Work across GTM Growth, RevOps, Data, Sales, and partnered Engineering teams to connect product changes to business outcomes. What We’re Looking For - 10+ years in a quantitative role such as Data Science, Product Analytics, Decision Science, or Applied Statistics, ideally at a product-led company supporting B2B growth, revenue systems, sales, lifecycle, or scaled self-serve motions. - Deep grounding in experimentation, causal inference, and applied statistics, with experience designing and interpreting tests in real-world, always-on environments. - Strong technical fluency in SQL and Python, including working directly with messy, incomplete operational and behavioral data to extract signal and quantify impact. - Proven track record translating analysis into shipped decisions, not just readouts: changes to product, routing, targeting, automation, policy, workflow design, or GTM strategy. - Strong business judgment and a bias toward action. You can scope ambiguous problems, define success, choose the highest-leverage measurement path, and move quickly from insight to decision. - Systems thinking and technical maturity. You can reason about agents, workflows, instrumentation, data quality, human review, feedback loops, and operational constraints together. - Excellent communication and cross-functional partnership. You can influence PMs, engineers, GTM operators, DS peers, and senior leaders with clear recommendations and practical tradeoffs. NICE TO HAVE - Experience with LLMs, AI agents, agent evaluation, or AI-assisted operations platforms. - Experience with sales, prospecting, RevOps, lifecycle, support, routing, prioritization, or operational automation systems. - Experience building measurement, experimentation, observability, or attribution frameworks from scratch in early-stage or rapidly evolving environ