Senior Applied Scientist, Seller Growth Science

Senior Applied Scientist, Seller Growth Science Join us in the evolution of Amazon’s Seller business! The Seller Growth Science organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We are looking for a Senior Applied Scientist to lead us to identify data-driven insight and opportunities to improve our SP growth strategy and drive new seller success. As a successful applied scientist on our talented team of scientists and engineers, you will solve complex problems to identify actionable opportunities, and collaborate with engineering, research, and business teams for future innovation. Key responsibilities include: Identify opportunities to improve SP growth and development process and translate those opportunities into science problems via principled statistical solutions. Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in MLOps. Design and lead roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers. Work with our engineering partners and draw upon your experience to meet latency and other system constraints. Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. Be responsible for communicating our science innovations to the broader internal & external scientific community. Basic Qualifications We require: 4 years of applied research experience 3 years of building machine learning models for business application experience PhD, or Master's degree and 6 years of applied research experience Experience programming in Java, C++, Python or related language Experience with neural deep learning methods and machine learning Preferred Qualifications Preferred qualifications include: Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. Experience with large scale distributed systems such as Hadoop, Spark etc. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. J-18808-Ljbffr

Location: Asti, Piemonte, IT

Posted Date: 10/14/2025