About the job
The Amazon Fulfillment Technologies (AFT) Science team is looking for an exceptional Applied Scientist, with strong optimization and analytical skills, to develop production solutions for one of the most complex systems in the world: Amazon’s Fulfillment Network. At AFT Science, we design, build and deploy optimization, simulation, and machine learning solutions to power the production systems running at world wide Amazon Fulfillment Centers.
Responsibilities
Develop an understanding and domain knowledge of operational processes, system architecture and functions, and business requirements
Deep dive into data and code to identify opportunities for continuous improvement and/or disruptive new approach
Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions for existing and new challenges
Create prototypes and simulations for agile experimentation of devised solutions
Advocate technical solutions to business stakeholders, engineering teams, and senior leadership
Partner with engineers to integrate prototypes into production systems
Design experiment to test new or incremental solutions launched in production and build metrics to track performance
Qualifications
Minimum
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.
Preferred
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