Senior Applied Scientist, Experience Analytics

Amazon • Seattle, WA

1d • Onsite

About The Position

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.

Requirements

• 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)

Nice To Haves

• Experience with customer analytics, behavioral segmentation, or user modeling 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

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.

Benefits

• sign-on payments
• restricted stock units (RSUs)
• 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
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree