About the job
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.
Responsibilities
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
Qualifications
Minimum
3+ years of building models for business application experience
PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
Experience in patents or publications at top-tier peer-reviewed conferences or journals
Experience programming in Java, C++, Python or related language
Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred
Experience using Unix/Linux
Experience in professional software development
Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.