Senior Applied Scientist, Experience Analytics

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 segmentation models behavioural classifiers and predictive frameworks 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 teams work in customer segmentation models behavioral classification systems and predictive frameworks 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 teams 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 Earths 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.

- PhD in computer science mathematics statistics machine learning or equivalent quantitative field
- 5 years of experience building and deploying ML models into production systems
- Experience programming in Python or equivalent with production-quality code
- Experience with ML frameworks (e.g. PyTorch TensorFlow scikit-learn) and ML infrastructure (training pipelines model serving monitoring)

- Experience with customer analytics behavioral segmentation or user modelling at scale
- Experience with real-time ML systems (online scoring streaming data anomaly detection)
- Experience working with large-scale customer data platforms or data lake architectures
- Experience with AWS data and ML services (SageMaker Redshift Athena Glue or equivalent)
- Published research in relevant ML or applied science venues
- Experience mentoring and contributing to science hiring processes
- Experience working in teams where models must ship not just perform well in notebooks

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including health insurance (medical dental vision prescription Basic Life & AD&D insurance and option for Supplemental life plans EAP Mental Health Support Medical Advice Line Flexible Spending Accounts Adoption and Surrogacy Reimbursement coverage) 401(k) matching paid time off and parental leave. Learn more about our benefits at WA Seattle - 167100.00 - 226100.00 USD annually