Applied Scientist II, Worldwide Returns & Recommerce - Science

* Design, develop, and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision Models. * Use SQL to query and analyze the data. * Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. * Use machine learning and analytical techniques to create scalable solutions for business problems. * Research and implement novel machine learning and statistical approaches. * Mentor interns. * Work closely with data & software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. BASIC QUALIFICATIONS - 2+ years of building models for business application experience - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - 3+ years of programming in Java, C++, Python or related language experience - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - Experience with popular deep learning frameworks such as MxNet and Tensor Flow PREFERRED QUALIFICATIONS - Experience building machine learning models or developing algorithms for business application - Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects) - Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms - Experience in patents or publications at top-tier peer-reviewed conferences or journals

Amazon is a global technology company operating in e-commerce, cloud computing (AWS), digital streaming, advertising and artificial intelligence. Its India operations span development centres in Bengaluru, Hyderabad, Chennai, Pune, Gurugram and Mumbai, covering retail technology, AWS infrastructure, Alexa, advertising, payments and supply-chain systems.