Senior Applied Scientist, Amazon Shopping Personalization

Amazon Amazon · Big Tech · NY +1 · Machine Learning Science

Senior Applied Scientist role focused on researching, designing, and developing new AI technologies for Amazon's Personalization and recommendation systems. The role involves inventing, experimenting with, and launching new features and products using large-scale datasets and computational resources. Key responsibilities include building state-of-the-art models, conducting experiments, and collaborating with engineers and product managers to implement solutions end-to-end.

What you'd actually do

  1. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to optimize the shopping experience, and run these models directly on the retail website.
  2. Develop AI solutions for Recommendation systems using Deep learning, LLMs, Reinforcement Learning, distillation, and Optimization methods;
  3. Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end;
  4. Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses;
  5. Effectively communicate technical and non-technical ideas with teammates and stakeholders;

Skills

Required

  • 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

Nice to have

  • 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

  • state of the art
  • novel ideas
  • large datasets and tremendous computational resources
  • state-of-the-art models
  • Publish your research findings in top conferences and journals

Other signals

  • inventing, experimenting with, and launching new features, products and systems
  • build state-of-the-art models to optimize the shopping experience
  • run these models directly on the retail website
  • Design and conduct offline and online (A/B) experiments to evaluate proposed solutions