Applied Scientist - Large-Scale ML for Content Strategy

Amazon Science; Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon Science is seeking an Applied Scientist to build machine learning and AI models that simulate customer behavior at scale. You will work with stakeholders across business, finance, engineering and science to translate data-driven findings into actionable insights.

The role focuses on reinforcement learning, causal inference, and designing agentic AI systems, with collaboration across teams to productionize innovations and drive business impact.

Qualifications

• 3+ years of building machine learning models or developing algorithms for business applications.
• PhD, or Master’s degree and 4+ years of CS, CE, ML or related field experience.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.
• Experience programming in Java, C++, Python or related language.

Responsibilities

• Build models that simulate customer behavior to answer counterfactual questions guiding content and product investments.
• Apply deep learning, reinforcement learning, causal inference to large-scale data.
• Design and prototype agentic AI systems and novel ML approaches to improve model scale and accuracy.
• Validate models against real-world experiments and deliver production-ready systems with engineers.
• Translate model outputs into clear recommendations for business, finance, and science stakeholders.

Skills

ML models

Algorithms

Publications

Programming languages

Education

PhD or Master’s in CS/CE/ML or related

Tools

Java

C++

Python

Job description

Amazon Science is seeking an Applied Scientist to build machine learning and AI models that simulate customer behavior at scale. You will work with stakeholders across business, finance, engineering and science to translate data-driven findings into actionable insights.

The role focuses on reinforcement learning, causal inference, and designing agentic AI systems, with collaboration across teams to productionize innovations and drive business impact.