Applied Scientist, Amzn Shipping-Prd & Tech, Amzn Shipping-Prd & Tech

Job Description

Amazon Shipping Team: Basic Qualifications: Btech/Mtech in Computer Science, Machine Learning, Operations Research, Statistics, or related technical field applying ML techniques to solve complex business problems programming skills in Python, R, or similar languages Experience with modern ML frameworks (PyTorch, TensorFlow, etc.) About the Role: At Amazon Shipping, were revolutionizing package delivery through machine learning. Our network handles packages daily with predictive monitoring, proactive failure detection, and intelligent redundancy all while optimizing costs for our customers. Key Responsibilities: Design and develop ML models for: Transportation cost auditing and discrepancy detection Package-level shipping cost prediction First Mile optimization through warehouse pickup forecasting Delivery delay prediction using network signals and external factors Collaborate with cross-functional teams to implement ML solutions at scale Author scientific papers for ML conferences Mentor team members in ML best practices Provide ML consultation across organizations Preferred Qualifications: PhD in Experience with large-scale distributed systems Publication record in top-tier ML conferences Expertise in time series forecasting and anomaly detection Background in transportation/logistics optimization communication skills with technical and non-technical stakeholders Our Team: Youll join a diverse team of Applied Scientists, Software Engineers, and Business Intelligence Engineers working on edge ML solutions. Were passionate about solving complex problems and delivering customer value through innovation. 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 as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. 3+ years of building machine learning models for business application experience Experience programming in Java, C++, Python or related language Experience with neural deep learning methods and machine learning Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. Experience with large scale distributed systems such as Hadoop, Spark etc.