Applied Scientist Ii, Amazon Fulfillment Technology (aft) Science

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

Applied Scientist II role focused on developing and deploying optimization, ML, and GenAI/LLM solutions for Amazon's Fulfillment Network. The role involves designing and building scalable mathematical models and production systems to improve operational efficiency, with a strong emphasis on translating research into production-grade code.

What you'd actually do

  1. Develop deep understanding and domain knowledge of operational processes, system architecture, and business requirements
  2. Dive deep into data and code to identify opportunities for continuous improvement and disruptive new approaches
  3. Design and develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and emerging challenges
  4. Create prototypes and simulations for agile experimentation of proposed solutions
  5. Advocate for technical solutions with 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
  • 3+ years of building models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Relevant industry applied research experience in operations research, optimization, ML/AI, statistics, or an equivalent field

Nice to have

  • PhD with industry applied research experience and expertise in Operations Research, Optimization, ML/AI, Statistics, or an equivalent field
  • Experience with labor planning and staffing optimization problems
  • Proven track record of translating research into production systems and deploying production-grade code

What the JD emphasized

  • develop production solutions
  • develop optimization-driven solutions
  • design and develop scalable mathematical models for production systems
  • develop innovative, scalable, and reliable science-driven production solutions
  • partner with software engineers to integrate prototypes into production systems
  • deploying production-grade code

Other signals

  • develop optimization-driven solutions
  • design and develop scalable mathematical models for production systems
  • partner with software engineers to integrate prototypes into production systems
  • design and execute experiments to test new or incremental solutions launched in production