Applied Scientist II, EC2 Core Platform
Amazon - Seattle, Washington, USA
Posted Apr 15, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Applied Scientist II, EC2 Core Platform Seattle, Washington, USA Join the EC2 Capacity Intelligence Science Team in improving customer experience and capacity utilization by inventing and implementing scientific solutions directly into production where we have various complex online capacity management systems at the heart of where supply meets demand. We are seeking analytical problem-solvers who thrive on deep dives into data. They should be excited about applying scientific principles and statistical techniques to real-world challenges. The ideal candidate will possess strong multi-tasking abilities and can credibly bridge the gap between engineering teams and business stakeholders. Your critical thinking skills, business understanding, and technical savvy will be leveraged to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your domain spans the design, development, testing, and deployment of data-driven and highly scalable machine learning solutions in efficient capacity and demand allocation, and improving customer experience. An Applied Scientist in the EC2 Capacity Intelligence Science Team owns and drives medium-to-large sized projects to achieve cross-team goals. They invent and drive or heavily influence the design of scientifically-complex software solutions or systems, applying strong scientific reasoning and developing state-of-the-art models to solve complex problems for EC2 businesses. To successfully develop novel solutions to complex problems in this space, they must have expertise in at least one computer science discipline (e.g., Machine Learning, Deep Learning) and a good understanding of the relative strengths and weaknesses of various state-of-the-art scientific approaches such as Forecasting, Machine Learning, Deep
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