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Applied Scientist, Experience Analytics
Amazon
On-sitemidPosted May 5, 2026
About the Role
Develop and productionize ML models for customer behavior analysis, build and maintain ML infrastructure, and contribute to the scientific direction of the team.
Requirements
Requires scientific depth in signal analysis, pattern discovery, and predictive modeling, combined with production engineering skills to build and deploy ML infrastructure.
Full Job Description
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 — particularly around behavioral signals from AI agents and agentic workflows.
- Extend and invent scientific techniques where needed, while also knowing when existing approaches are sufficient, and speed matters more than novelty.
- Collaborate with engineers building the CLARA platform, the Experience Metrics Framework, and the Customer Segmentation Framework to ensure ML systems integrate cleanly and serve the broader product vision.
- Contribute to the team's scientific direction — proposing new modelling initiatives, sharing approaches, and helping the team make good trade-offs between rigor and velocity.
- Mentor others and contribute to the broader applied science community.
- Write clear technical documentation describing your approaches, trade-offs, and results.
About the team
Diverse Experiences AWS 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, has not followed a traditional path, or includes alternative experiences, do not let it stop you from applying.
Mentorship and Career Growth We are continuously raising our performance bar as we strive to become Earth's Best Employer. That is why you will find endless knowledge-sharing, mentorship, and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there is nothing we cannot achieve in the cloud.
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 — particularly around behavioral signals from AI agents and agentic workflows.
- Extend and invent scientific techniques where needed, while also knowing when existing approaches are sufficient, and speed matters more than novelty.
- Collaborate with engineers building the CLARA platform, the Experience Metrics Framework, and the Customer Segmentation Framework to ensure ML systems integrate cleanly and serve the broader product vision.
- Contribute to the team's scientific direction — proposing new modelling initiatives, sharing approaches, and helping the team make good trade-offs between rigor and velocity.
- Mentor others and contribute to the broader applied science community.
- Write clear technical documentation describing your approaches, trade-offs, and results.
About the team
Diverse Experiences AWS 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, has not followed a traditional path, or includes alternative experiences, do not let it stop you from applying.
Mentorship and Career Growth We are continuously raising our performance bar as we strive to become Earth's Best Employer. That is why you will find endless knowledge-sharing, mentorship, and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there is nothing we cannot achieve in the cloud.