Applied Scientist II (Machine Learning), ITA - Automated Performance Evaluation
ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise.
Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time.
You’ll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives.
Together, we can solve the world's toughest hiring problems.
Key job responsibilities
As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create technical roadmaps and drive production level projects that will support Amazon Science.You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
About the team
The Automated Performance Evaluation (APE) team is a hybrid team of Applied Scientists and Software Development Engineers who develop, deploy and own end-to-end machine learning services for use in the HR and Recruiting functions at Amazon.- PhD, or a Master's degree and experience in CS, CE, ML or related field- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in building machine learning models for business application- Experience using Unix/Linux
- Experience in professional software development
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