2 of 2 Reinforcement Learning Jobs in Edinburgh

Last Mile Channel Allocation • Using LLMs to automate analytical processes and insight generation • Ops research to optimize middle mile truck routes • Working with global partner science teams to affect Reinforcement Learning based pricing models and estimating Shipments Per Route for $MM savings • Deep Learning models to synthesize attributes of addresses • Abuse detection models to reduce network losses … Key job responsibilities 1. Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes 2. Design, develop, evaluate and deploy, innovative and highly scalable ML/OR models 3. Work closely with other science and … engineering teams to drive real-time model implementations 4. Work closely with Ops/Product partners to identify problems and propose machine learning solutions 5. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance 6. Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation More ❯

is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and … customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include … but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such More ❯