Applied Scientist, Amazon Music - Catalog Quality

Applied Scientist, Amazon Music - Catalog Quality

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Learn more at https://www.amazon.com/music.

The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines.

Key job responsibilities
As an Applied Scientist, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Other responsibilities include:

- Collaborate with scientists, engineers, and product managers to define and frame business problems as ML or optimization tasks.
- Use machine learning, deep learning, LLMs and Agentic AI techniques to create scalable solutions for business problems
- Analyze and extract relevant information from large amounts of Amazon's data to help automate and optimize key processes
- Design, development and evaluation of AI models for predictive learning
- Research and implement novel machine learning and statistical approaches
- Implement scalable data pipelines and model-serving systems.
- Analyze experimental results, draw insights, and refine models to improve accuracy and robustness.
- Communicate findings and recommendations to technical and non-technical audiences.

Basic Qualifications

- Experience programming in Java, C++, Python or related language
- Bachelor's degree or above in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
- Experience building machine learning models or developing algorithms for business application
- Experience with theory and practice of design of experiments and statistical analysis of results

Preferred Qualifications

- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
- Experience researching about machine learning, deep learning, NLP, computer vision, data science

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.