"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- Master's degree in computer science, machine learning, statistics, mathematics or equivalent quantitative field
- Experience building deep learning or machine learning models for business application
- Deep knowledge in one or more areas of machine learning or deep learning
- Experience programming in Java, Python or related language- PhD in computer science, machine learning, statistics, mathematics or equivalent quantitative field
- Experience in patents or publications at top-tier peer-reviewed conferences or journal
- Experience with NLP and generative deep learning models (i.e. LLM)
- Ability to work collaboratively in a cross-functional team environment
- Ability to communicate with both technical and business stakeholders
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit
https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,400/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit
https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
