Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP organization's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!
Agentic Advertiser Guidance team is focussed on guiding and supporting advertisers to meet their advertising needs of creating and managing ad campaigns. Our vision is to build a highly personalized, context aware agentic advertiser guidance that is built using LLMs and tools such as auction simulations, ML and optimization algorithms. This agent will be part of a larger agentic framework, working with chat and non-chat experiences across the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we work closely with stakeholders on Ad Console, and Amazon Sales team, to identify opportunities for the advertiser ranging from guidance across products to granular controls such as keywords and deliver them through a personalized experience.
We are looking for a passionate Senior Applied Scientist who has technical expertise in information retrieval, Large Language Models (LLM), optimization algorithms, Online Advertising, and/or randomized experiments. In addition to having hands-on experience in building ML-based solutions, an ideal candidate should be able to create and articulate a customer-centric science vision, show willingness to continuously learn about new scientific approaches, and enjoy operating in startup-like environment.