Applied Scientist: Network & Logistics Optimization (NA)

Amazon Inc.; Factoria (WA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

RSU program

Job summary

Amazon.com Services LLC in Bellevue, Washington is seeking an Applied Scientist to design and deploy advanced optimization and machine learning models that improve the middle-mile network and customer experience. You will own business challenges from day one, with autonomy to influence decisions in an ambiguous environment.

The role emphasizes academic rigor, scalable product development, and the ability to work independently within a team of researchers and engineers.

Qualifications

• PhD or Master’s with 4+ years CS/CE/ML related experience.
• 2+ years of programming in Java, C++, Python or related language.
• Experience building machine learning models or algorithms for business applications.
• 3+ years of mathematics optimization such as linear programming and nonlinear optimization.

Responsibilities

• Design and develop advanced mathematical optimization and machine learning solutions in network design and optimization.
• Research, prototype, simulate, and experiment with these models using Java and Python; participate in production deployment.
• Create, enhance, and maintain technical documentation and science designs.
• Present to Scientists, Product, and Software Engineering teams, as well as Stakeholders.
• Lead project plans from a scientific perspective by managing features, risks, milestones and launches.

Skills

Java

Python

C++

Machine Learning

Optimization

Education

PhD in Operations Research/Statistics/Engineering/Supply Chain

MS/MA in CS/CE/ML with 4+ years experience

Job description

Amazon.com Services LLC in Bellevue, Washington is seeking an Applied Scientist to design and deploy advanced optimization and machine learning models that improve the middle-mile network and customer experience. You will own business challenges from day one, with autonomy to influence decisions in an ambiguous environment.

The role emphasizes academic rigor, scalable product development, and the ability to work independently within a team of researchers and engineers.