Sr Applied Scientist, Amazon Fulfillment Tech - Science

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

Sr. Applied Scientist role focused on developing and deploying optimization, ML, and GenAI/LLM solutions for Amazon's fulfillment network. The role involves understanding operational processes, designing mathematical models, creating prototypes, partnering with engineers for production integration, and monitoring performance. Requires experience in building and deploying optimization and ML models at scale.

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 6+ years of applied research experience
  • Experience programming in Java, C++, Python or related language
  • 3+ years of building machine learning models for business application experience
  • 3+ years of experience building and deploying optimization models for business applications at scale
  • Excellent written and verbal communication skills, with the ability to convey complex technical concepts to diverse audiences

Nice to have

  • PhD with 6+ years of industry applied research experience and expertise in Operations Research, Optimization, ML/AI, Statistics, or an equivalent field
  • Demonstrated success translating research into production systems and deploying production-grade code
  • Proven track record of leading, mentoring, and growing scientists
  • Ability to thrive in a fast-paced team environment, delivering robust technical solutions with complex dependencies and cross-functional requirements
  • Experience in fulfillment, supply chain, logistic systems

What the JD emphasized

  • building machine learning models for business application experience
  • building and deploying optimization models for business applications at scale

Other signals

  • develop production solutions
  • optimization, statistics, machine learning, and GenAI/LLM solutions
  • power production systems
  • deploy optimization models for business applications at scale