Applied Scientist, Amazon

• PhD, or a Master's degree and experience in CS, CE, ML or related field
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience applying theoretical models in an applied environment
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Experience programming in Java, C++, Python or related language
• Ability to deliver under tight deadlines

Desirable

• The Discovery Tech team helps customers discover and engage with new, popular and relevant products across Amazon worldwide
• 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
• 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
• The responsibilities of 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
• The Applied Scientist engages with product and marketing stakeholders directly to explain and dive deep into the problems, which are often not well defined
• This engagement is sustained from product conception all the way to product launch
• For example, they review, with the product and marketing stakeholders, the A/B experiment setup, customer problems, hypothesis, method and the impact metrics to ensure that business objectives are met