Senior Applied Scientist, Personalization

Description

Do you want to transform the way people shop at Amazon? We are looking for stellar applied scientists to be part of our multi-disciplinary team. We are re-imagining the future of the Amazon shopping experience for customers through tailoring it to their current intent. We understand what customers are interested in shopping for, and guide them to discover what they need as they shop.

If you love building new technology that helps customers solve their problems, this is your opportunity to impact millions of customers. Come help us build the future of personalized shopping at Amazon!

As a Senior Applied Scientist on the team, you will help lead science innovation in our space across a large multidisciplinary team. You will have a breadth of problems to work on, ranging from developing state of the art LLM-based techniques to reason about customers and products, developing deep learned transformer-based models to understand and abstract customer intent signals and representations, building large-scale real-time multi-task ranking systems, and more.

You will build technology employed by teams across the company, while also having a direct connection to millions of customers through our own customer facing features every day. Come join us in the journey!

• Innovate new features and models that have huge impact on the customer experience. Help customers find the right products and content on their shopping journey. Leverage the use of advanced machine learning to create customer shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime
• Be a key leader on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers
• Drive the science roadmap across multiple teams, helping coordinate a cohesive science agenda across the org.
• Mentoring applied scientists across the org, growing their skills and careers.

Our vision is to build the next generation of shopping experience at Amazon through personalization and understanding the customer's intent. We imagine our core experiences to work together as a talented personal shopping assistant would - a partner that is knowledgeable, understands your preferences, and helps you find the right solution for your needs. We aim for the quality of personalization to be a core reason customers choose Amazon, on par with Earth's largest selection, low prices, and fast and free shipping.

Our team is part of Amazon's Personalization organization, a high-performing group that leverages Amazon's expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. We run global experiments and our work has revolutionized e-commerce with features such as "Keep shopping for ...", "Customers who bought this item also bought", and "Frequently bought together".

Amazon's internal surveys regularly recognize us as one of the best engineering organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continual opportunity to learn and grow.

• 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

• 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. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or…