Senior Applied Scientist, International Machine Learning

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Applied Science

This role focuses on building and deploying machine learning models for consumer business applications within Amazon's International Machine Learning team. The scientist will analyze large datasets, design, develop, evaluate, and deploy scalable predictive models, and work with engineering teams to implement these models in production. The role also involves establishing automated processes for model development and maintenance, and mentoring other scientists and engineers.

What you'd actually do

  1. Use machine learning and analytical techniques to create scalable solutions for business problems
  2. Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes
  3. Design, development, evaluate and deploy innovative and highly scalable models for predictive learning
  4. Research and implement novel machine learning and statistical approaches
  5. Work closely with software engineering teams to drive real-time model implementations and new feature creations

Skills

Required

  • machine learning
  • statistical techniques
  • predictive learning
  • deep learning
  • Python
  • Java
  • C++

Nice to have

  • R
  • scikit-learn
  • Spark MLLib
  • MxNet
  • Tensorflow
  • numpy
  • scipy
  • Hadoop
  • Spark

What the JD emphasized

  • 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

Other signals

  • building and deploying advanced algorithmic systems
  • analyze and extract relevant information from large amounts of Amazon’s historical business data
  • design, development, evaluate and deploy innovative and highly scalable models for predictive learning
  • Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production