Applied Scientist II: Fulfillment Optimization & ML

Socket.dev; Bellevue (WA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Comprehensive health benefits

RSU opportunities

401(k) match

Job summary

Amazon Fulfillment Technologies (AFT) Science team seeks an exceptional Applied Scientist with strong optimization and analytical skills to develop production solutions for our large-scale fulfillment network.

You will design, implement, and deploy optimization, simulation, and machine learning models to improve labor planning, pick assignment, scheduling, and flow processes across Fulfillment Centers, collaborating with engineers and stakeholders.

Qualifications

• PhD or Master's with 4+ years of relevant experience.
• 2+ years building models for business applications.
• Experience programming in Java, C++, or Python.
• Applied research in OR, optimization, ML, or statistics.

Responsibilities

• Develop an understanding of operational processes, system architecture and business requirements.
• Deep dive into data and code to identify opportunities for improvement or disruptive new approaches.
• Develop scalable mathematical models for production systems to derive optimal or near-optimal solutions.
• 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 experiments to test new or incremental solutions launched in production and build metrics to track performance.

Skills

Modeling for business

Programming in Java/C++/Python

Operations research

Applied research in ML/OR

Education

PhD

Master's degree

Tools

Java

C++

Python

Job description

Amazon Fulfillment Technologies (AFT) Science team seeks an exceptional Applied Scientist with strong optimization and analytical skills to develop production solutions for our large-scale fulfillment network.

You will design, implement, and deploy optimization, simulation, and machine learning models to improve labor planning, pick assignment, scheduling, and flow processes across Fulfillment Centers, collaborating with engineers and stakeholders.