Amazon Development Center U.S., Inc.
Technology
SeniorAppliedScientist,ExperienceAnalytics
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Applied Scientist, Experience Analytics at Amazon Development Center U.S., Inc.. Skills: Machine Learning, Data Analysis, Statistical Modeling, Data Engineering. Develop and deploy machine learning models. Design and implement data pipelines”
Industry & Context.
Root cause analysis; Troubleshooting
What They're Looking For.
Must Have
5+ years of experience in data science or related field, Proficiency in SQL, Experience with Python or R
Nice to Have
PhD in a quantitative field, Experience with cloud platforms (AWS, GCP, Azure), Experience with ML frameworks (scikit-learn, TensorFlow, PyTorch), Experience with BI tools (Tableau, Power BI, Looker)
What You'll Do.
Develop and deploy machine learning models
Design and implement data pipelines
Build and maintain BI dashboards
Perform statistical analysis and A/B testing
Collaborate with engineering and product teams
Communicate findings to stakeholders
Stay up-to-date with industry trends
How You'll Work.
Team & Collaboration
Cross-functional teams; Product teams; Engineering teams
Communication Scope
Stakeholder presentations
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 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
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