Are you passionate about solving unique customer-facing problem in the Amazon scale? Are you excited by developing and productizing machine learning, deep learning algorithms and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match!
Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience.
The mission of Size/Fit science team as part of Fashion Tech is to innovate and develop scalable ML solutions to provide personalized size and fit recommendations when Amazon Fashion customers evaluate apparels or shoes online. The team is hiring an experienced Applied Scientist who has a solid background in applied Machine Learning, including computer vision, recommendation systems and generative AI.
Key job responsibilities
Key job responsibilities
- You will work on our Science team and partner closely with other scientists, data engineers as well as product managers, UX designers, and business partners to answer complex problems with novel scientific approaches.
- You can navigate ambiguous problems by working backward from customer needs to propose and develop effective scientific solutions.
- You have excellent communication skills to be able to work with cross-functional team members to understand key questions and earn the trust of senior leaders.
- You are able to multi-task between different tasks such as gap analysis of algorithm results, integrating multiple disparate datasets, doing business intelligence, analyzing engagement metrics or presenting to stakeholders.
- You thrive in an agile and fast-paced environment on highly visible projects and initiatives.
