About the job
How can we improve the customer experience by tailoring what we display on our pages based on available data? How do we build models that help us innovate in different ways to enhance customer experience? What is the relationship between what customers do on the site vs. what they actually buy? How do we do all of this without asking the customer a single question? Our team's stated missions is to 'grow each customer’s relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations.' Recommendations at Amazon is a way to help customers discover products. Our team strives to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day.
Responsibilities
Ask research questions about customer behavior; build state-of-the-art models to generate recommendations; run these models directly on the retail website; participate in the Amazon ML community; mentor Applied Scientists and software development engineers with a strong interest and knowledge of ML; measure impact using scientific tools.
Qualifications
Minimum
PhD in Computer Science, Statistics, Operations Research, Economics, or a related quantitative field; 3+ years of experience applying machine learning techniques to solve business problems; experience building large-scale machine learning systems; experience with statistical modeling, machine learning algorithms, and data mining techniques.
Preferred
Experience with large-scale distributed systems; experience with recommendation systems; experience with A/B testing and causal inference; experience mentoring junior scientists; publication record in top-tier conferences or journals (e.g., KDD, ICML, NeurIPS, WWW, SIGIR).