Senior Applied Scientist, Scot Oss - Sourcing Execution & Performance

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

Senior Applied Scientist role focused on building Gen AI solutions for supply chain optimization, including agent-based collaboration and dispute evaluation. The role involves leading science teams, developing algorithmic approaches, and deploying production-scale models, with a secondary focus on inference pipelines.

What you'd actually do

  1. Provide technical leadership and mentorship to the Science team, setting the standard for methodological rigor, peer review, and innovation across all workstreams
  2. Build solutions to enable collaborative inventory planning with vendors through agent to agent collaboration or humans-in-the loop collaborative methods
  3. Pioneer Gen AI solutions for dispute evaluation and vendor coaching, defining the technical approach, evaluating model performance against business outcomes, and establishing responsible AI guardrails for production deployment
  4. Drive the full development cycle from whiteboarding new algorithmic approaches to production-scale deployments
  5. Collaborate with SDEs to build high-performance, distributed training and inference pipelines; translate complex scientific concepts into scalable, production-grade code

Skills

Required

  • building machine learning models for business application
  • PhD, or Master's degree and 6+ years of applied research experience
  • programming in Java, C++, Python or related language
  • neural deep learning methods
  • machine learning

Nice to have

  • modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • large scale distributed systems such as Hadoop, Spark etc.
  • supply chain optimization
  • operations research
  • vendor management systems

What the JD emphasized

  • Build solutions to enable collaborative inventory planning with vendors through agent to agent collaboration or humans-in-the loop collaborative methods
  • Pioneer Gen AI solutions for dispute evaluation and vendor coaching
  • evaluating model performance against business outcomes
  • establishing responsible AI guardrails for production deployment
  • production-scale deployments
  • building scalable measurement solutions
  • models are robust, monitored, and maintainable in production environments

Other signals

  • Gen AI solutions for dispute evaluation and vendor coaching
  • agent to agent collaboration or humans-in-the loop collaborative methods
  • production-scale deployments
  • scalable measurement solutions
  • automation of analysis processes