"We see our customers as invited guests to a party, and we are the hosts. It's our job every day to make every important aspect of the customer experience a little bit better." - Jeff Bezos, Founder & CEO.
To help achieve this, the Worldwide Defect Elimination (WWDE) team, within Amazon Customer Service, relentlessly focuses on maintaining customer trust by building products that offer appropriate resolutions to resolve issues faced by our customers. WWDE scientists solve complex problems and build scalable solutions to help our customers navigate through issues and eliminate systemic defects to prevent future issues.
As an Applied Scientist, your role is pivotal in leveraging advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to address customer issues at scale. You'll develop innovative solutions that summarize and detect issues, organize them using taxonomy, and pinpoint root causes within Amazon systems. Your expertise will drive the identification of responsible stakeholders and enable swift resolution. Utilizing the latest techniques, you will build an AI ecosystem that can efficiently comb over our billions of customer interactions (using a combination of media). As a part of this role, you will collaborate with a large team of experts in the field and move the state of defect elimination research forward. You should have a knack for leveraging AI to translate complex data insights into actionable strategies and can communicate these effectively to both technical and non-technical audiences.
Key job responsibilities
- Develop ML/GenAI-powered solutions for automating defect elimination workflows
- Implement scalable and efficient scientific solutions in production environments
- Design and implement robust metrics to evaluate the effectiveness of ML/AI models
- Perform statistical analyses and statistical tests, including hypothesis testing and A/B testing
- Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation
