The Everyday Essentials team at Amazon is looking for an experienced and highly motivated Applied Scientist to solve complex problems around improving the profitability of Everyday Essential items offered through Amazon.com. These initiatives will enhance how Amazon connects customers with local inventory, creating a more efficient shopping experience with faster delivery options for customers and lower operational costs for Amazon. The initiatives will improve Amazon’s retail operations by reducing the cost of delivering to customers and providing them with relevant and locally available purchase options at faster speeds. The Applied Scientist will determine which products benefit most from regional availability by analyzing customer preferences and supply chain conditions.
Key job responsibilities
* Scale optimization techniques to drive business value
* Design A/B tests and conduct statistical analysis on their results
* Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers
* Present and publish science research, contributing to Amazon's science community
* Mentor junior engineers and scientists
* Work closely with internal stakeholders including business teams, engineering teams, and partner teams to align them with your focus area
About the team
Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including supervised and unsupervised machine learning, non-convex optimization, causal inference, natural language processing, linear programming, reinforcement learning, and other forecast algorithms. Some critical research areas in our space include modeling substitutability between similar products, incorporating basket awareness and complementarity-aware logic, measuring speed sensitivity of products, modeling network capacity constraints, and supply and demand forecasting.
