Principal Applied Scientist, Amazon Stores Economics & Science (seas)

Amazon Amazon · Big Tech · Palo Alto, CA · Applied Science

Principal Applied Scientist role focused on applying machine learning, optimization, and economics to improve Amazon's Stores business, specifically in areas like delivery speed, seller fees, and LLM applications. The role involves leading a team, developing scientific models, benchmarks, and services, and deploying solutions in partnership with product teams.

What you'd actually do

  1. lead a team of scientists and engineers with backgrounds in machine learning, NLP, IR, statistics, and economics to identify bottlenecks in our business, conceive new ideas to overcome those challenges, and deploy scientific solutions in partnership with product teams.
  2. developing the scientific models, benchmarks, and services.
  3. apply expertise in science and engineering to move from local to global optima in methods, models, and software.
  4. leveraging frontier science
  5. collaborating with partner teams

Skills

Required

  • building machine learning models for business application
  • programming in Java, C++, Python or related language
  • neural deep learning methods
  • machine learning
  • Transformers
  • LLMs
  • deep learning techniques

Nice to have

  • causal inference
  • forecasting models
  • modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • large scale distributed systems such as Hadoop, Spark etc.

What the JD emphasized

  • Graduate education and hands-on experience in machine learning, optimization, causal inference, Bayesian statistics, deep learning, or other quantitative scientific fields is a must.
  • Practical knowledge of how we can leverage Transformers, LLMs, or other deep learning techniques for a variety of applications is a must.

Other signals

  • applying expertise in science and engineering to move from local to global optima in methods, models, and software
  • leveraging frontier science
  • developing the scientific models, benchmarks, and services
  • applying machine learning, optimization, causal inference, Bayesian statistics, deep learning, or other quantitative scientific fields