Applied Scientist, Central Machine Learning

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

Applied Scientist role focused on building and deploying machine learning models for Amazon's consumer businesses. Responsibilities include analyzing large datasets, designing and evaluating scalable models, and collaborating with engineering teams for real-time implementation. The role emphasizes end-to-end ownership of business problems and optimizing operations through ML.

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

  • building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field 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 in data applications using large scale distributed systems (e.g., EMR, Spark, Elasticsearch, Hadoop, Pig, and Hive)
  • Proficient in any two of these areas: large language models, NLP (Information retrieval, Machine Translation), Computer Vision, Classification models using Boosting/Bagging or Deep Neural Networks.

What the JD emphasized

  • building models for business application experience
  • Experience in 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
  • own end-to-end business problems/metrics
  • drive real-time model implementations
  • establish scalable, efficient, automated processes