Machine Learning Applied Scientist
il y a 3 jours
Ph.D degree in Machine Learning, Computer Science, Mathematics, Statistics or related quantitative field.
- At least 2 years of professional experience with phd in a relevant field: machine learning, deep learning, knowledge bases, recommendation systems, information retrieval, natural language understanding, robotics, statistics, computer vision, etc.
- Fundamentals in problem solving, computer science, algorithm design, complexity analysis, mathematics and statistics.
- Proficiency in at least one major programming language (Java, C++, Python, Scala or similar). The more the merrier.
- Good verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
Amazon is looking for a passionate, talented, and inventive applied scientist with Machine Learning and optionally Search background to join the International Search team. As an applied scientist, you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the team's mission—real world incarnations of some of the most difficult academic problems.
**Responsibilities**:
- Work on research projects in machine learning and related fields to create highly innovative customer experiences;
- Analyze large amounts of data to discover patterns, find opportunities, and develop highly innovative, scalable algorithms to seize these opportunities;
- Validate models via statistically rigorous experiments across millions of customers;
- Work closely with software engineering teams to build scalable prototypes for testing, and integrate successful models and algorithms in production systems at very large scale;
- Work side by side with fellow applied scientists in the team.
Our team is located in our European headquarters in Luxembourg, in the heart of Europe.
**About Luxembourg**:
Luxembourg, LUX
- Previous work in information retrieval.
- Practical experience in building machine learning models from A to Z, from problem definition to deployment.
- Practical experience in building and evaluating models using major libraries such as MxNet, TensorFlow or Scikit-Learn.
- Experience in scripting languages is a plus.
- Experience working on gigabyte and terabyte-size datasets
- Experience working in text processing, data mining, and/or data visualisation is a plus.
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