Principal Applied Scientist - AMZ9794093

Amazon•Seattle, WA

4d•$198,900 - $269,000•Onsite

About The Position

Amazon.com is seeking a Principal Applied Scientist to participate in the design, development, evaluation, deployment, and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. This role involves developing and/or applying statistical modeling techniques, optimization methods, and other ML techniques to various business and engineering applications. The scientist will build and deploy ML models, run and analyze experiments in a production environment, identify new research opportunities to meet business goals, and research and implement novel ML and statistical approaches. Additionally, the role includes mentoring junior engineers and scientists.

Requirements

Master’s degree or foreign equivalent degree in Computer Science, Machine Learning, Engineering, or a related field and five years of research or work experience in the job offered, or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation.
Alternatively, a Bachelor’s degree or foreign equivalent degree in Computer Science, Machine Learning, Engineering, or a related field and seven years of progressive post-baccalaureate research or work experience in the job offered or a related occupation.
One year of research or work experience in programming in Java, C++, Python, or equivalent programming language.
One year of research or work experience in conducting the analysis and development of various supervised and unsupervised machine learning models for moderately complex projects in business, science, or engineering.

Nice To Haves

Please see job description and the position requirements above.

Responsibilities

Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications.
Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering.
Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment.
Identify new opportunities for research in order to meet business goals.
Research and implement novel ML and statistical approaches to add value to the business.
Mentor junior engineers and scientists.