Amazon brings buyers and sellers together. Our retail customers depend on us to give them access to every product at the best possible price. Our sellers depend on us to give them a platform to launch their business into every home and marketplace. Making this happen is the mission of every scientist in North America Stores (NAS) organization.
To this end, the Science team is tasked with:
· Building and deploying AI / ML models and LLM-powered systems that lead to hundreds of millions in business impact across supply chain optimization, customer engagement, and cultural relevance at Amazon scale
· Partnering with product teams in evaluating the financial and operational impact of new product offerings.
· Partnering with science teams across other organizations to develop state of the art algorithms and models.
· Carrying out independent data-backed initiatives that can be leveraged later on in the fields of network organization and financial modeling of processes.
· Publishing papers in both internal and external conferences / journals.
In order to execute the above mandate we are on the look out for smart and qualified Applied Scientists who will own projects in partnership with product and research teams as well as operate autonomously on independent initiatives that are expected to unlock benefits in the future. Our team builds science-backed systems that directly influence vendor negotiations, forecasting, buying, product discovery for secondary language customers, and inventory management for North America's retail business.
Key job responsibilities
As an Applied Scientist, you are able to use a range of artificial intelligence and operations research methodologies to solve challenging business problems when the solution is unclear. Key responsibilities include:
Develop workflows that combine ML models with optimization engines, similarity search, and human-in-the-loop capabilities to automate complex business processes
Build scalable data and inference pipelines using AWS services (SageMaker, Bedrock, FAISS, Andes) to process 100M+ ASINs and serve real-time predictions in production
Design and execute rigorous experimentation frameworks including weblabs, IPC labs, and causal inference methods to validate model impact and drive launch decisions
Collaborate cross-functionally with engineering, product, and business teams to translate ambiguous business problems into well-scoped science solutions with clear success metrics
