Product Manager Intern, JST, 2024 Shanghai
网申链接：https://www.amazon.jobs/en/jobs/2626277/product-manager-intern-jst-2024-shanghai
Looking for an opportunity to move fast and make a global impact? Japan Store Tech (JST) oversees every technical aspect for Amazon Stores business in Japan, from enhancing user experience to optimizing site merchandising, and from fostering customer engagement to streamlining the supply chain. To meet JP unique customer needs, JST collaborates with Amazon's global teams and leverage Amazon most advanced technologies, while also creates tailor-made tech solutions. Our aspiration is clear: to be the most innovative tech team, the most reliable partner for our business, and the most beloved workplace for our employees.
We are looking for a Product Manager intern to deep dive GenAI and Large Language Model (LLM) and their application to JP ecommerce. As a successful candidate, you will have experience deep diving new business areas, gathering industry trends, conducting business analysis, applying business judgements and providing fact based recommendations. You have a strong bias toward data driven decision making, tech driven solution designing and an innate ability to understand how metrics relate to business problems and to each other. You should be comfortable with an extremely high degree of ambiguity, can work across a wide cross section of stakeholders, and relish the idea of solving challenging problems. You will tackle novel, situations every day and given the size of this initiative, you’ll have the opportunity to work with Amazon teams around the globe.
· 3+ years in product/program management, finance, or retail category management
· MBA or MS in Business Administration, Finance, or equivalent experience
· Experience in ecommerce, supply chain, AI are highly valued
· Product Management background – experience owning and delivering end-to-end solutions and experiences, ability to product and program manage business work streams and have experience executing with a high bar within compressed timelines