Sr Manager, Applied Science, Creative Intelligence

Requirements

• PhD in Machine Learning, Statistics, or Computer Science (or MS + 10 years equivalent applied experience)
• 8+ years building and shipping production ML systems
• 5+ years managing teams of 10+ scientists or ML engineers
• Experience with real-time serving systems and online learning at scale
• Track record of measurable business impact from deployed models
• Experience in recommendation systems, causal inference, or multi-armed bandits
• Background in ad-tech creative optimization or dynamic content personalization
• Prior role at a peer platform (Meta, Google, TikTok) in ads or creative science
• Strong point of view on where creative optimization intersects with auction design and platform economics
• Comfort defining strategy in ambiguity rather than executing a handed roadmap
• Publications or patents in relevant areas (but production impact valued over publication count)
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• USA, NY, New York - 240,600.00 - 325,500.00 USD annually
• USA, WA, SEATTLE - 218,800.00 - 295,900.00 USD annually

Additional Information

Lead the science organization powering Dynamic Creative Optimization (DCO) and Creative Brain (CB) across Amazon Ads. Own the models, algorithms, and research agenda that drive measurable advertiser performance lift through creative personalization at impression level. Manage a team of applied scientists and data sciences building the closed-loop system where every impression generates signal and every signal improves the next creative decision. Key job responsibilities Own the models that personalize ad creative at serving time across all formats. Drive optimization beyond clicks toward conversion, consideration, and long-term advertiser value. Compress the learning loop from days to hours. Expand model-driven optimization from partial to full coverage. Solve cold-start and self-competition problems. Build the persistent memory layer so every campaign inherits the intelligence of the last. Develop causal inference that isolates which creative components drive lift and why. Create cross-advertiser priors that raise the floor for new advertisers on day one. Design the representation architecture that lets the system reason about creative quality before serving. Own quality science: defect detection, compliance, aesthetics. Define science strategy for new surfaces and segments. Lead competitive analysis against peer platforms. Ensure research translates to production in quarters, not years. Build and manage a team of 8+ applied and data scientists

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Company Intel

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