Data Scientist, Maple - Recommender System

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

This role focuses on developing and deploying personalized recommendation systems for Amazon customers, integrating GenAI techniques, and conducting research. The primary deliverable is a customer-facing AI product.

What you'd actually do

  1. Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization.
  2. Develop and test new signals for improving recommendation models
  3. Integrate GenAI into Amazon customer shopping experience
  4. Design A/B tests and conduct statistical analysis on their results
  5. Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers

Skills

Required

  • 2+ years of data scientist experience
  • 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
  • 1+ years of guiding and coaching a group of researchers experience
  • 1+ years of working with or evaluating AI systems experience
  • Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
  • Experience applying theoretical models in an applied environment

Nice to have

  • Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
  • Knowledge of machine learning concepts and their application to reasoning and problem-solving
  • Experience in Python, Perl, or another scripting language
  • Experience in a ML or data scientist role with a large technology company
  • Experience in defining and creating benchmarks for assessing GenAI model performance
  • Experience working on multi-team, cross-disciplinary projects
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions
  • Experience effectively communicating complex concepts through written and verbal communication

What the JD emphasized

  • state-of-the-art recommendation system modeling
  • GenAI techniques
  • publishing research
  • GenAI model performance

Other signals

  • personalization
  • recommendation systems
  • GenAI
  • research
  • A/B testing