Applied Scientist, Amzn Shipping-Prd & Tech

1 - 6 years

50 - 85 Lacs

Posted:1 day ago| Platform: Naukri logo

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Full Time

Job Description

  • 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! Experience programming in Java, C++, Python or related language Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse Experience building machine learning models or developing algorithms for business application Experience implementing algorithms using both toolkits and self-developed code Have publications at top-tier peer-reviewed conferences or journals

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