The 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. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. Our interests and activities span machine learning for better ranking, experimentation, statistics for better decision making, and infrastructure to make it all happen efficiently at scale.Īmazon Advertising is one of Amazon's fastest growing and most profitable businesses. About the team We are a team consisting of software engineers and applied scientists. You will also find yourself in meetings with business and tech leaders at Amazon communicating your next big initiative. On a day-to-day this means building ML models, analyzing data from your recent A/B tests, and guiding teams on best practices. A day in the life Our primary focus is improving search ranking systems. In addition to typical topics in ranking, we are particularly interested in evaluation, feature selection, explainability. As a Senior Applied Scientist you will find the next set of big improvements to ranking evaluation, get your hands dirty by building models to help understand complexities of customer behavior, and mentor junior engineers and scientists. Key job responsibilities You will build search ranking systems and evaluation framework that extend to Amazon scale - thousands of product types, billions of queries, and hundreds of millions of customers spread around the world. In practice, we aim to create infrastructure and metrics, enable new experimental methods, and do proof-of-concept experiments, that enable Search Relevance teams to introduce new features faster, reduce the cost of experimentation, and deliver faster against Search goals. This team’s charter is to innovate and evaluate ranking at Amazon Search. We are seeking a strong applied Scientist to join the Experimentation Infrastructure and Methods team. Amazon’s large scale brings with it unique problems to solve in designing, testing, and deploying relevance models. Our Search Relevance team works to maximize the quality and effectiveness of the search experience for visitors to Amazon websites worldwide. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Search services go to work. The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies.
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