Applied Scientist Iii, Amzn Shipping-prd

DESCRIPTIONOur customers have immense faith in our ability to deliver packages timely and as expected A well planned network seamlessly scales to handle millions of package movements a day It has monitoring mechanisms that detect failures before they even happen such as predicting network congestion operations breakdown and perform proactive corrective actions When failures do happen it has inbuilt redundancies to mitigate impact such as determine other routes or service providers that can handle the extra load and avoids relying on single points of failure service provider node or arc Finally it is cost optimal so that customers can be passed the benefit from an efficiently set up network Amazon Shipping 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 actual shipping cost You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost Using signals from within the transportation network such as network load and velocity of movements derived from package scan events and outside such as weather signals you will build models that predict delivery delay for every package These models will help improve buyer experience by triggering early corrective actions and generating proactive 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 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 You will work as part of a diverse data science and engineering team comprising of other Applied Scientists Software Development Engineers and Business Intelligence Engineers You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML You will also be called upon to provide ML consultation outside your team for other problem statements If you are excited by this charter come join us BASIC QUALIFICATIONS- 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 learningPREFERRED QUALIFICATIONS- 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 - Master s degree in math statistics engineering or other equivalent quantitative discipline or PhDOur inclusive culture empowers Amazonians to deliver the best results for our customers If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information If the country region you re applying in isn t listed please contact your Recruiting Partner