Amazon Sr Applied Scientist Compliance Shared Services CoSS 

United States, Washington, Seattle 

790196095

Key job responsibilities
• Design and evaluate state-of-the-art algorithms and approaches in multi-modality fusion, large language models, continual learning, and federated learning
• Extend and invent new algorithms and scientific approaches that improve on the state-of-the-art to decrease Amazon’s cost to serve
• Identify and drive scientist productivity improvements across science teams=
• Collaborate with product and tech partners and customers to validate hypothesis, drive adoption, and increase business impact
• Key author in writing high quality scientific papers in internal and external peer-reviewed conferences.
• Lead cross-organization working groups to develop science foundational capabilities applicable to multiple use cases beyond compliance for the broader Customer Trust and Selling Partner Services (SPS) organizations.
A day in the life
- Writing code, and running experiments with re-usable science libraries
- Reviewing labels and audit results with investigators and operations associates

- PhD, or Master's degree and 5+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with conducting research in a corporate setting
- Proficiency in developing re-usable Python libraries
- Peer-reviewed publications in multi-modality, large language models, continual learning and/or federated learning.
- Ability to convey Machine Learning concepts and considerations to non-experts

- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience leading long-term science direction, developing medium-term hypothesis plans, and short-term execution and delegation across 6+ scientists in science teams.
- Significant citations in peer-reviewed scientific contributions in relevant fields
- Practical work experience in building, iterating, deploying and maintaining re-usable python libraries code in end-to-end production systems