Applied Scientist Ii, Central Machine Learning

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

This role focuses on building and deploying advanced machine learning models for consumer business applications, optimizing business operations and profitability. It involves end-to-end ownership from data analysis and model development to implementation and scaling, with a strong emphasis on creating automated processes for model lifecycle management.

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

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 3+ years of building models for business application experience
  • 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

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • building models for business application experience
  • patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • build and deploy advanced algorithmic systems
  • analyze and model terabytes of data
  • design, development, evaluate and deploy innovative and highly scalable models
  • research and implement novel machine learning and statistical approaches
  • drive real-time model implementations
  • establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation