Drimva

Amazon

Data Scientist II, Amazon Private Brands, PBI Selection Guidance

$136K–$184K

San Diego, CA · Full-time · Posted Sep 29, 2026

Experience
2+ years of data scientist experience; 3+ years in querying/scripting or statistical/mathematical software; 3+ years in machine learning/statistical modeling and data analysis; 1+ years creating or contributing to textbooks, research papers, or educational content.
Education
Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in STEM.

About the job

Develops and guides data science solutions—from research and prototypes through production—to improve Amazon Private Brands’ product selection and business decisions.

What you'll do

  • Partner with business stakeholders to frame business problems as science problems; analyze data and build pipelines to support decisions.
  • Invent science solutions, create prototypes, and deploy production software; review and guide the team’s science work.
  • Share research internally and externally, and establish repeatable applied science practices.

What they ask for

  • 3+ years of experience with data querying or scripting languages, or statistical/mathematical software.
  • 3+ years of experience with machine learning/statistical modeling, data analysis tools and techniques, and performance parameters.
  • 1+ years of experience creating or contributing to mathematical textbooks, research papers, or educational content.
  • Master's degree in STEM, or experience working in 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; large technology company experience and cross-disciplinary project experience are also preferred.

Pay

136,000.00 - 184,000.00 USD annually (base salary)

Tools

SQL, Python, R, SAS, Matlab, Perl

AI focus

Applies Generative AI, machine learning, agentic AI, and large language models to product assortment and business decisions; research areas include named entity recognition and product substitutes.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 5, 2026 (UTC)