DESCRIPTION
Are you excited to help customers discover the hottest and best-reviewed products?
The Discovery Tech team helps customers discover and engage with new, popular, and relevant products across Amazon worldwide. We do this by combining technology, science, and innovation to build new customer-facing features and experiences alongside cutting-edge tools for marketers. You will be responsible for creating and building critical services that automatically generate, target, and optimize Amazon's cross-category marketing and merchandising. Through the enablement of intelligent marketing campaigns that leverage machine-learning models, you will help to deliver the best possible shopping experience for Amazon's customers all over the globe.
We are looking for analytical problem solvers who enjoy diving into data, are excited about data science and statistics, can multi-task, and can credibly interface between engineering teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your domain spans the design, development, testing, and deployment of data-driven and highly scalable machine learning solutions in product recommendation.
As an Applied Scientist, you bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions.
Key job responsibilities
The responsibilities of the Applied Scientist in the DiscoTec science team include creating state-of-the-art AI solutions to rank both customers' purchase journeys and ASINs using scalable big data tools in collaboration with Sr. scientists and Amazon Scholars. The solutions are broad and include ML content retrieval, classical Bayesian statistics (e.g. Maximum Likelihood, Good-Turing Estimators), graph neural networks, contextual bandits (learning feedback loop), and computer vision. The AS covers this breadth of knowledge and has a bias towards simple, practical, and extensible solutions.
BASIC QUALIFICATIONS PhD, or a Master's degree and experience in CS, CE, ML, or related fieldExperience in patents or publications at top-tier peer-reviewed conferences or journalsExperience applying theoretical models in an applied environmentExperience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computingExperience programming in Java, C++, Python, or related languageAbility to deliver under tight deadlines. PREFERRED QUALIFICATIONS PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics, or equivalent quantitative fieldExperience in professional software developmentExperience in solving business problems through machine learning, data mining, and statistical algorithmsExperience in designing experiments and statistical analysis of resultsExperience with popular deep learning frameworks such as MxNet and Tensor Flow
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify, and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use, and transfer the personal data of our candidates.
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