Data Scientist Ii, Amazon Private Brands

Amazon Amazon · Big Tech · CA, BC +1 · Applied Science

Data Scientist II at Amazon Private Brands focused on applying Generative AI, Machine Learning, Statistics, and Economics to product assortment, business decisions, and product inputs. The role involves investigating business problems, inventing novel solutions, prototyping, and deploying production software, with research areas including NER, product substitutes, pricing optimization, agentic AI, and LLMs. The Data Scientist will also guide other scientists and publish research.

What you'd actually do

  1. Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions.
  2. Perform data analysis and build data pipelines to drive business decisions.
  3. Invent novel science solutions, develop prototypes, and deploy production software to solve business problems.
  4. Review and guide science solutions across the team.
  5. Publish and socialize your and the team's research across Amazon and external avenues as appropriate

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 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

  • 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 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

  • novel technology
  • novel solutions
  • science research
  • applied science practices

Other signals

  • Generative AI
  • Machine Learning
  • Statistics
  • Economics solutions
  • named entity recognition
  • product substitutes
  • pricing optimization
  • agentic AI
  • large language models