An information-rich and accurate product catalog is a strategic asset for Amazon. It powers unrivaled product discovery, informs customer buying decisions, offers a large selection, and positions Amazon as the first stop for shopping online. The Catalog Relationship team within Amazon is working to innovate in the space of inferring, managing, and presenting relationships between items in the catalog to drive better product discovery and customer experience while navigating Amazon's large and ever-growing catalog. We use data analysis and statistical and machine learning techniques to proactively identify and fix item-item relationships within the Amazon product catalog. This problem is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality).
The Catalog Relationships group is looking for an innovative and customer-focused senior applied scientist to help us make the world’s best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will work in a collaborative environment where you can experiment with massive data from the world’s largest product catalog, work on challenging problems, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers.
- PhD or Masters in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related quantitative field and strong knowledge of machine learning.
5+ years of relevant experience in industry and/or academia.
Fluency in at least one programming language (C++, Java, or similar) and one scripting language (Perl, Python, or similar).
Familiarity with a broad set of supervised and unsupervised ML approaches and techniques ranging from Regression to Deep Neural Networks.
Proven track record of successfully applying ML-based solutions to complex problems in business, science, or engineering.
Experience with fast prototyping.
Experience working effectively with software engineering teams.
Ph.D. in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related quantitative field
10+ years of relevant experience in industry and/or academia.
Publications at top-tier peer-reviewed conferences or journals.
Depth and breadth in state-of-the-art computer vision and machine learning technologies.
Good written and spoken communication skills.
Experience with modern methods for parallelized processing of large, distributed datasets (e.g. Spark, Hadoop, Map-Reduce).
- Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation