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Applied Scientist/Machine Learning

Job ID: 427545 | Amazon Corporate LLC

DESCRIPTION

Are you experienced at applying machine learning to big-data tasks? Are you excited by analyzing and modeling terabytes of text, images, and other types of data to solve real-world problems? We love data and we have lots of it. We’re looking for top scientists capable of using machine learning and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

The machine learning organization at Amazon has multiple positions available for applied scientists in Seattle, Palo Alto, Berlin, New York, Austin, Cupertino, Manhattan and Bangalore for machine learning experts at all career stages, from graduating doctoral students to internationally renowned researchers and practitioners.

We solve many of Amazon's most difficult and important problems, and in partnership with teams across Amazon, we build new services that surprise and delight our customers. We have current and future projects in video recommendation, streaming data analysis, natural language processing, deep learning, bandit algorithms, computer security, social networks, and more.

Machine learning at Amazon is a highly experimental activity, although theoretical analysis and innovation are also welcome. Our applied scientists work closely with software engineers to put algorithms into practice. They also work on cross-disciplinary efforts with social scientists, computer vision experts, and others.

If interested please apply directly online via this posting and our recruiting department will review your profile.

BASIC QUALIFICATIONS

· PhD in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field
· 3+ years of hands-on experience in predictive modeling and analysis
· Strong algorithm development experience
· Skills with Java, C++, or other programming language, as well as with R, MATLAB, Python or similar scripting language

PREFERRED QUALIFICATIONS

· 1-8+ years of relevant experience in industry and/or academia.