Applied Scientist - Machine Learning, Amazon Transportation

Amazon Amazon · Big Tech · Bellevue, WA · Research Science

This role focuses on inventing and building machine learning models for Amazon's transportation network, specifically for trailer imbalance forecasting and safety stock optimization. The scientist will develop models from prototype to production, translate business problems into modeling approaches, and influence business decisions worth billions of dollars.

What you'd actually do

  1. Design and develop machine learning, forecasting, and other prediction models that enhance our optimization and planning systems.
  2. Build models and algorithms from prototype to production-level systems.
  3. Translate ambiguous business problems into modeling approaches, and drive the technical design with product, engineering, and operations partners.
  4. Influence key business decisions through rigorous modeling and analysis.
  5. Communicate results and recommendations to scientific and business audiences.

Skills

Required

  • PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience
  • Experience building machine learning models or developing algorithms for business application
  • 1+ years of programming in Java, C++, Python or related language experience
  • Experience in standard machine-learning and statistical modeling tools and techniques (e.g. random forests, gradient-boosted regression, LASSO, logistic regression)

Nice to have

  • Experience in professional software development
  • Experience with forecasting and statistical analysis
  • Experience in optimization mathematics such as linear programming and nonlinear optimization

What the JD emphasized

  • invent new approaches and algorithms
  • machine learning, forecasting, and prediction models
  • prototype to production-level systems

Other signals

  • invent new approaches and algorithms
  • develop machine learning, forecasting, and prediction models
  • build models and algorithms from prototype to production-level systems