Applied Scientist, International Machine Learning

Amazon Amazon · Big Tech · IN, HR, Gurugram · Machine Learning Science

This role focuses on building and deploying advanced ML systems to optimize business operations and customer experience within Amazon's International Emerging Stores. The scientist will analyze large datasets, develop and evaluate ML models, and work with engineering teams for production implementation, impacting millions of transactions and business metrics.

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, develop, evaluate and deploy, innovative and highly scalable ML models
  4. Work closely with software engineering teams to drive real-time model implementations
  5. Work closely with business partners to identify problems and propose machine learning solutions

Skills

Required

  • building models for business application experience
  • PhD, or Master's degree and 1+ years of CS, CE, ML or related field experience
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
  • Experience with any programming language such as Python, Java, C++

Nice to have

  • Experience implementing algorithms using both toolkits and self-developed code
  • Have publications at top-tier peer-reviewed conferences or journals

What the JD emphasized

  • building models for business application experience
  • scalable solutions
  • automate and optimize key processes
  • highly scalable ML models
  • real-time model implementations
  • large-scale complex ML models in production
  • large scale data analyses
  • model development
  • model validation
  • model maintenance

Other signals

  • building and deploying advanced ML systems
  • optimize millions of transactions
  • analyze and model terabytes of data
  • end-to-end business problems/metrics
  • drive real-time model implementations
  • drive the implementation of large-scale complex ML models in production