Advertising Marketing Science Lead
OpenAISan FranciscoPosted 7 April 2026
Job Description
Advertising Marketing Science Lead
About the team
OpenAI’s mission is to ensure the responsible and widespread adoption of artificial intelligence. In support of that mission, the Marketing team helps deeply understand customer audiences and market dynamics, influence the development of the right products, build sustainable and customer-aligned monetization models, and drive awareness, adoption, and usage across OpenAI’s products and platform.
We take a data-driven approach to understand markets, develop monetization strategies, and uncover customer needs that shape product strategy and messaging. We partner closely with Sales, Partnerships, Product, Engineering, Research, Comms, and Design to deliver a cohesive end-to-end customer experience and lead go-to-market efforts for new product launches across channels.
About the role
We’re looking for an Advertising Marketing Science leader to establish and scale OpenAI’s advertiser-facing reporting, measurement, and attribution credibility. You’ll combine deep measurement expertise with strong judgment and cross-functional leadership to define how advertisers understand performance on OpenAI and how our reporting aligns with their existing measurement frameworks (MTA, incrementality/lift testing, MMM/geo experimentation).
This role will start as a hands-on individual contributor responsible for building the methodological foundations of OpenAI’s advertising measurement system. Over time, you will define the strategy, operating model, and team needed to scale this function globally as advertiser adoption grows.
This role is ideal for someone who enjoys building new capabilities from first principles, can translate complex causal measurement approaches into trusted industry narratives, and is energized by partnering across Product, Engineering, Sales, Partnerships and Legal to build a privacy-first measurement ecosystem.
In this role, you will:
- Define OpenAI’s advertiser measurement strategy, establishing how our reporting aligns with attribution (MTA), incrementality/lift testing, MMM, geo experimentation, and partner measurement frameworks.
- Build the foundation of OpenAI’s Marketing Science function, initially leading work as an individual contributor while designing the long-term team structure, operating model, and measurement programs.
- Lead advertiser-facing measurement discussions, representing OpenAI in executive briefings, measurement escalations, and industry conversations while building trust in our methodologies and reporting.
- Develop clear advertiser narratives that translate causal inference, attribution models, and statistical methodologies into understandable guidance for campaign optimization and investment decisions.
- Design and govern OpenAI’s advertising measurement program, including standardized experiment patterns (A/B, geo, quasi-experimental), power calculators, diagnostics, and experiment-quality guardrails.
- Build scalable measurement frameworks that reconcile results across MMM, MTA, and lift testing, helping advertisers triangulate OpenAI performance within their broader marketing measurement systems.
- Establish privacy-centric measurement approaches as needed, including aggregated measurement, and conversion modeling in partnership with Legal and Privacy teams.
- Translate measurement strategy into product capabilities, partnering with Product and Engineering to operationalize methodologies into durable measurement tools and reporting infrastructure.
- Shape OpenAI’s external measurement ecosystem, working with third-party measurement partners, clean-room providers, and industry groups to align standards and reduce friction for advertisers.
You might thrive in this role if you:
- Have deep expertise in advertising measurement including experimentation, incrementality testing, attribution modeling, and econometric approaches such as MMM.
- Have experience designing and scaling lift or incrementality programs, including govern ... (truncated, view full listing at source)
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