Applied Scientist II, Automated Inventory Management
Job ID: 2794941 | Amazon.com Services LLC
Amazon’s Automated Inventory Management (AIM) team is looking for passionate, hard-working, and talented individuals to join our fast-paced, stimulating environment to help invent the future of business ownership with Technology, and to translate big data into actionable insights.
The AIM team is part of the Supply Chain Optimization Technology (SCOT) Team within the Operations Organization. As an Applied Scientist on the AIM team, you will design quantitative systems, prediction models and solve real-world problems using the latest machine learning techniques. You will also work with a team of Product Managers, Business Intelligence Engineers, and Software Engineers to research and build solutions to provide insights to business leaders at the most senior levels throughout the company.
Key job responsibilities
- Implement statistical and machine learning methods to solve complex business problems
- Research new ways to improve predictive and explanatory models
- Directly contribute to the design and development of automated prediction systems and ML infrastructure
- Build models that can detect supply chain defects and explain variance to the optimal state
- Collaborate with other researchers, software developers, and business leaders to define the scientific roadmap for this team
BASIC QUALIFICATIONS
- 2+ years of building models for business application experience
- PhD, or Master's degree and 1+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
PREFERRED QUALIFICATIONS
- Experience using Unix/Linux
- Experience in professional software development
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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