Data Scientist, Private Brand Analytics

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

Data Scientist on the Core Tech Private Brands Analytics team, responsible for building and improving forecasting and planning models for Amazon Private Brands. This involves end-to-end pipeline development, replacing manual processes with code-driven solutions, and evaluating model accuracy. The role partners with business, science, and tech stakeholders and contributes to the organization's AI framework and experimentation rigor.

What you'd actually do

  1. build and improve forecasting and planning models across APB
  2. partnering with business, science, and tech stakeholders
  3. end-to-end pipeline development (feature engineering through training and deployment) on SageMaker, S3, and Datanet
  4. replacing manual spreadsheet-driven processes with reproducible code-driven pipelines and dashboards
  5. evaluating model accuracy across business segments

Skills

Required

  • 1+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.)
  • 2+ years of data/research scientist, statistician or quantitative analyst in an internet-based company with complex and big data sources experience
  • 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
  • Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)

Nice to have

  • Knowledge of statistical packages and business intelligence tools such as SPSS, SAS, S-PLUS, or R
  • Knowledge of machine learning concepts and their application to reasoning and problem-solving
  • Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
  • Experience working with or evaluating AI systems
  • 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

  • strong fundamentals in forecasting and applied ML
  • experience with Python and SQL
  • comfort working with large-scale retail datasets
  • ability to communicate findings clearly to non-technical partners

Other signals

  • forecasting and planning models
  • end-to-end pipeline development
  • feature engineering through training and deployment
  • evaluating model accuracy