Take the earth's most customer-centric company. Mix in millions of shoppers spending billions of dollars annually and an opportunity to use your skills in machine learning and data mining to improve product recommendations and search. What do you get? The best job in the internet today - PERIOD.
Amazon’s Personalization team owns recommending the right product to the right customer at the right time. We revolutionized e-commerce with features like Customers Who Bought Also Bought That, Buy it Again and Recommended for You.
The Recommendations Modeling team at Amazon.com generates personalized product recommendations for millions of customers each day, in a blink of an eye, thousands of times a second. We build the data sets, algorithms and back-end systems to deliver a personalized store for every Amazon.com customer.
We are an applied research team, a combination of highly skilled engineers and scientist, who are working on the next generation of recommendations to improve our customer’s experience with Amazon. We experiment rapidly using the latest machine learning techniques such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs) and Matrix Factorization. We work with Machine Learning experts across Amazon to deliver the best possible recommendations, leveraging Amazon’s vast computing resources (AWS) and data. We deal with large amounts of training data, rapid prototyping, offline/online testing and high-performance requirements.
- Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key features and processes.
- Work closely with software engineering teams to drive new feature creation
- Work closely with stakeholders to optimize various business operations
- Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
- Track general business activity and provide clear, compelling management reporting on a regular basis
- Research and implement novel statistical approaches