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Amazon Research Scientist AWS Supply Chain 
United States, Washington, Bellevue 
168786704

04.03.2025
DESCRIPTION

As an Research Scientist, you’ll research, design, model and prototype state-of-the-art models and solutions used by users worldwide. As part of your role you will regularly interact with other scientists, software engineering teams and business leadership. The focus of this role is to research, develop, and deploy models to improve state-of-the-art of supply chain capabilities including demand forecasting and supply planning. You will have the opportunity to work on our assistant solution allowing our users to ask data questions in natural language and get intelligent insights and exceptions.Key job responsibilities
Develop accurate and scalable machine learning models or optimization solutions to solve our hardest supply chain problems.A day in the life
Why AWSDiverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Work/Life BalanceInclusive Team CultureMentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

BASIC QUALIFICATIONS

- 3+ years of investigating the feasibility of applying scientific principles and concepts to business problems and products experience
- PhD, or Master's degree and 5+ years of quantitative field research experience
- Experience with big data technologies such as AWS, Hadoop, Spark, Pig, Hive etc.
- Experience communicating qualitative research methods and findings to non-qualitative researchers


PREFERRED QUALIFICATIONS

- Experience converting research studies into tangible real-world changes
- Experience with discrete and continuous optimization methodologies and algorithms