Are you interested in computational advertising and sponsored recommendations? Do you thrive in a fast-paced organization with a significant impact on hundreds of millions of customers? Do you love to innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems? If so, Amazon Sponsored Products could be the right place for you.
The Amazon Sponsored Products (SP) Off-Search team is focused on building delightful ad experiences across various surfaces on Amazon, such as product detail pages, homepage, store-in-store pages, to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, stays relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Stores stakeholders to lead the expansion and growth of SP across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value.
Key job responsibilities
We are seeking an Applied Science Manager to drive transformative innovation in ads allocation, marketplace intelligence, whole-page relevance to rethink how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. You will reimagine ads allocation mechanisms to holistically consider business drivers and shopper needs. This involves developing new auction functions that consider ad relevance, order product sales, ads revenue, cost-to-serve, and advertiser demand, while remaining sensitive to seasonality, holidays, and bespoke shopping experiences (e.g., premium or grocery shopping). You will lead the team to entail the design and testing of models and simulations, estimate how different auction parameters perform under varying shopper cohorts (e.g., first-time versus repeat shoppers), product categories, seller types (e.g., vendors versus sellers), and regional demand profiles, setting the foundation for adaptive, dynamic auction strategies that maximize value for both shoppers and advertisers. You will lead exciting frontiers where GenAI can play a pivotal role in shaping future allocation decisions.
As a senior leader, you will play a critical role in elevating the team’s scientific and technical rigor, identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. You will establish a long-term vision for continued scientific innovation, setting strategic goals to future-proof the organization’s technical stacks and ML/LLM frameworks to support new and emerging business objectives. Additionally, you will grow talents, fostering a culture of excellence and continuous learning to enhance the organization’s ability to solve complex problems in advertising science.
