Excited by Big Data, Machine Learning and Predictive Software? Interested in creating new state-of-the-art solutions using Machine Learning and Data Mining techniques on Terabytes of Data?
At Amazon Bangalore, we are developing state-of-the-art large-scale Machine Learning Services and Applications on the Cloud involving Terabytes of data. We work on applying predictive technology to a wide spectrum of problems in areas such as Amazon Retail, Seller Services, Customer Service, Alexa, Chatbots and so on. We are looking for talented and experienced Machine Learning Scientists (Ph.D. in a related area preferred) who can apply innovative Machine Learning techniques to real-world e-Commerce problems. You will get to work in a team dedicated to advancing Machine Learning technology at Amazon and converting it to business-impacting solutions.
· Use machine learning, data mining and statistical techniques to create new, 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
· Design, develop and evaluate highly innovative models for predictive learning
· Establish scalable, efficient, automated processes for large scale data analyses model development, model validation and model implementation
· Research and implement novel machine learning and statistical approaches
· A Masters and/or PhD in CS, Machine Learning, Operational research, Statistics or in a highly quantitative field.
· Experience in predictive modelling and analysis, predictive software development.
· Strong problem-solving ability
· Good skills with Java/Scala or C++, Perl/Python (or similar scripting language)
· Experience in using R, Matlab, or any other statistical software
· Strong communication and data presentation skills
· Experience handling gigabyte and terabyte size datasets
· Experience working with distributed systems and grid computing
· Knowledge of the latest and state of the art ML technology.
· Publications or presentation in recognized Machine Learning and Data Mining journals/conferences