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 team's 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 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.
Basic Qualifications
- 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)
Preferred Qualifications
- 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.
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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 https://amazon.jobs/en/benefits.
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually