Applied Scientist Ii, Amazon Fulfillment Technology , Amazon Fulfillment Technologies (aft)

Amazon Amazon · Big Tech · Bellevue, WA · Machine Learning Science

This role focuses on developing and deploying optimization, simulation, and machine learning solutions for Amazon's fulfillment network. The scientist will build scalable mathematical models, create prototypes, partner with engineers for production integration, and design experiments to test new solutions. The role requires a PhD or Master's with 4+ years of experience in a related field, and experience building models for business applications.

What you'd actually do

  1. Develop an understanding and domain knowledge of operational processes, system architecture and functions, and business requirements
  2. Deep dive into data and code to identify opportunities for continuous improvement and/or disruptive new approach
  3. Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and new challenges
  4. Create prototypes and simulations for agile experimentation of devised solutions
  5. Advocate technical solutions to business stakeholders, engineering teams, and senior leadership

Skills

Required

  • PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience
  • 2+ years of building models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Relevant industry or academic applied research experience in operations research, optimization, machine learning, statistics or an equivalent field.

Nice to have

  • PhD with applied research experience and expertise in Operations Research, Optimization, Machine Learning, Statistics, or an equivalent field
  • Experience in large scale optimization and decomposition techniques, planning and scheduling problems
  • Experience in building machine learning models and developing algorithms for business applications
  • Experience in developing and deploying code for production systems
  • Experience with exploratory data analysis and experimental design

What the JD emphasized

  • develop production solutions
  • develop innovative, scalable, and reliable science-driven solutions
  • PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience
  • 2+ years of building models for business application experience

Other signals

  • develop production solutions for one of the most complex systems in the world
  • design, build and deploy optimization, simulation, and machine learning solutions
  • develop innovative, scalable, and reliable science-driven solutions that are beyond the published state of art