NYC Staffing
Applied Scientist II, Marketplace Intelligence, Sponsored Products
NYC Staffing, New York, New York, United States, 10001
Applied Scientist Position
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. The Search Ranking and Interleaving team within the Marketplace Intelligence org in SP is responsible for determining which ads to show in Amazon search, where to place them, how many ads to place, and to which customers. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers', advertisers', and Amazon's interests. To do this, we apply a broad range of machine learning, causal inference, and optimization techniques to continuously explore, learn, and optimize the ranking and allocation of ads on the search page. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. We are looking for an Applied Scientist to join the Search Ranking team in MI. The team is responsible for improving the quality of ads shown to users (e.g., relevance, personalized and contextualized ranking to improve shopper experience and business metrics) via online experimentation, ML modeling, simulation, and online feedback. As an Applied Scientist on this team, you will identify big opportunities for the team to make a direct impact on customers and the search experience. You will work closely with search and retail partner teams, software engineers and product managers to build scalable real-time ML solutions. You will have the opportunity to design, run, and analyze A/B experiments that improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact while broadening your technical skillset. Key job responsibilities include tackling and solving challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. You will drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. You will develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. You will develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace and then evolve them. You will also research new and innovative machine learning approaches. Additionally, you will recruit scientists to the team and provide mentorship. Basic qualifications include 3+ years of building models for business application experience, a PhD or Master's degree and 4+ years of CS, CE, ML or related field experience, experience programming in Java, C++, Python or related language, and experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing. Preferred qualifications include experience using Unix/Linux and experience in professional software development.
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. The Search Ranking and Interleaving team within the Marketplace Intelligence org in SP is responsible for determining which ads to show in Amazon search, where to place them, how many ads to place, and to which customers. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers', advertisers', and Amazon's interests. To do this, we apply a broad range of machine learning, causal inference, and optimization techniques to continuously explore, learn, and optimize the ranking and allocation of ads on the search page. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. We are looking for an Applied Scientist to join the Search Ranking team in MI. The team is responsible for improving the quality of ads shown to users (e.g., relevance, personalized and contextualized ranking to improve shopper experience and business metrics) via online experimentation, ML modeling, simulation, and online feedback. As an Applied Scientist on this team, you will identify big opportunities for the team to make a direct impact on customers and the search experience. You will work closely with search and retail partner teams, software engineers and product managers to build scalable real-time ML solutions. You will have the opportunity to design, run, and analyze A/B experiments that improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact while broadening your technical skillset. Key job responsibilities include tackling and solving challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. You will drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. You will develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. You will develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace and then evolve them. You will also research new and innovative machine learning approaches. Additionally, you will recruit scientists to the team and provide mentorship. Basic qualifications include 3+ years of building models for business application experience, a PhD or Master's degree and 4+ years of CS, CE, ML or related field experience, experience programming in Java, C++, Python or related language, and experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing. Preferred qualifications include experience using Unix/Linux and experience in professional software development.