Applied Scientist, Experience Analytics
Amazon - Seattle, Washington, USA
Posted May 4, 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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- Verification
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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, Experience Analytics Seattle, Washington, USA AWS Experience Analytics (EXA) is seeking an Applied Scientist to join our team. EXA exists to turn customer understanding into products and intelligence that teams across AWS can use. We are building a unified customer lifecycle data platform, customer experience measurement frameworks, and segmentation systems, and the science that powers these products is well underway. What we need is someone who can add to our work in signal analysis, pattern discovery, and predictive modelling - bringing both scientific depth and the production engineering skills to take models from notebook to production. You will bring your creative and learn and be curious mindset and work within the science team helping us ship faster across the full range of modelling and ML work and at greater scale. The problems are genuinely interesting. AWS customers are shifting from console-based building toward AI-augmented, agent-primary, and autonomous workflows. The signals that tell us who customers are, what they are trying to do, and where they struggle are changing fundamentally. There is more to model, more to explore, and more to build than the current team can get to - and that is where you come in. Key job responsibilities - Contribute to and extend the team's work in signal analysis, pattern discovery, and predictive modelling - adding scientific depth and production engineering capability. - Build production ML infrastructure - offline training pipelines, online scoring systems, and monitoring. - Frame and tackle new modelling problems as they emerge -
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