At Amazon, we strive every day to be Earth’s most customer centric company. Selling Partner Support Engagement (SPSE) Science delivers on this by building AI-enhanced experiences and automation that help to provide world class support to our global network of selling partners. We are building at the cutting edge of Gen AI applications, working to tackle the many challenges that we confront caused by the volume, diversity, and complexity of our selling partner's needs… and we are always striving to do better.
Do you want to join an innovative team who creatively applies techniques ranging from statistics and traditional machine learning to deep learning, natural language processing, and generative models? A team that drives our flywheel of improvement by hunting down opportunities to do better that are buried in tens of millions of solved cases? Are you interested in helping us redefine what world class support can be in an age of automation and AI, while prizing human empathy and ingenuity?
The SPSE Science Team is looking for an Applied Scientist to build statistical and machine learning solutions that help us understand and solve our most challenging problems. We need to better understand our Sellers and the problems they face, to augment our human workforce with smarter tools, to anticipate problems so that we are prepared to deal with them, to automatically diagnose and resolve issues, and to identify opportunities to grow and improve.
In this role, you will have ownership of the end-to-end development of solutions to complex problems and you will play an integral role in strategic decision-making. You will also work closely with engineers, operations teams, and product owners to build ML pipelines, platforms, and solutions that solve problems of defect detection, automation, and workforce optimization.
BASIC QUALIFICATIONS
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
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