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OpenAI is seeking a Marketing Scientist to join its Ads Marketing Science function as a senior individual contributor. This role combines rigorous measurement expertise with client judgment and hands-on execution to help OpenAI Ads prove and improve advertisers' media performance.
You will partner closely with Sales, advertisers, and agencies to shape learning agendas, interpret results, and drive media performance improvements. Your responsibilities include applying rigorous advertising measurement approaches across attribution, incrementality testing, marketing mix modeling (MMM), and other methodologies. You'll develop scalable measurement programs and guidance that improve decision-making and adoption of rigorous measurement practices across the advertiser base.
Key focus areas include advancing incrementality measurement capabilities, onboarding measurement partners to privacy-forward workflows and integrations, and creating reusable programs, tools, agents, and workflows that enable Sales and clients to access high-quality Marketing Science support at scale.
You will shape advertiser-facing insights experiences that translate campaign and audience data into actionable performance recommendations, conduct auction optimization analyses to identify delivery and pricing opportunities, and communicate complex statistical concepts clearly to both technical and non-technical audiences while upholding high standards for privacy, quality, and transparency.
Ideal candidates bring 10+ years of deep expertise in advertising measurement, including experimentation, incrementality testing, attribution, and econometric approaches. You should be a hands-on senior contributor comfortable working directly with large datasets using SQL, Python, or R, and AI-enabled analytical workflows. Experience designing and operating lift and incrementality programs, working with third-party measurement providers, and partnering with Product and Engineering teams to operationalize measurement methodology is essential. You thrive in fast-paced, high-ambiguity environments, influence across teams without formal authority, and care deeply about privacy-forward, trustworthy measurement.
