Senior Data Scientist, Amazon Stores Finance Science, Amazon Stores Finance Science

Amazon Amazon · Big Tech · Seattle, WA · Data Science

This role focuses on developing and building new ML and statistical forecasting models for Amazon Stores' financials, aiming to improve financial decision-making and planning for senior leadership. It involves working with stakeholders, collaborating with various teams for production implementation, and adapting new modeling techniques.

What you'd actually do

  1. Demonstrating thorough technical knowledge, effective exploratory data analysis, and model building using industry standard ML models
  2. Working with technical and non-technical stakeholders across every step of science project life cycle
  3. Collaborating with finance, product, data engineering, and software engineering teams to create production implementations for large-scale ML models
  4. Innovating by adapting new modeling techniques and procedures
  5. Presenting research results to our internal research community

Skills

Required

  • 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
  • 4+ years of data scientist experience
  • Experience with statistical models e.g. multinomial logistic regression
  • Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
  • Experience managing data pipelines

Nice to have

  • Experience as a leader and mentor on a data science team
  • Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)
  • Master's degree
  • Experience formulating and solving predictive modeling, machine learning, forecasting or statistical modeling problems

What the JD emphasized

  • lead high visibility initiatives
  • develop new science-based forecasting methodologies
  • build scalable models
  • build new ML and statistical models from the ground up

Other signals

  • develop new science-based forecasting methodologies
  • build scalable models to improve financial decision making
  • build new ML and statistical models from the ground up