Job ID: 2808806 | Amazon.com Services LLC
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. We use data analysis and statistical and machine learning techniques to proactively identify relationships between products 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).
Amazon’s Item and Relationship Identity Systems group is looking for an innovative and customer-focused 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.
Key job responsibilities
- Map business requirements and customer needs to a scientific problem.
- Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization.
- Research, design and implement scalable machine learning (ML) techniques to solve problems that matter to our customers in an iterative fashion.
- Design, experiment and evaluate highly innovative models for predictive, explainable learning.
- Partner with other scientists to build state-of-the-art ML systems powering Amazon.
- Work closely with software engineering teams to drive real-time model experiments, implementations and new feature creations.
- Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
About the team
The IRIS team owns programs and systems to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog. We focus on the following areas: 1) reducing customer perceived duplicates: eliminating all duplicate ASINs that are indistinguishable by customers and identifying broken and missing variations, 2) reducing product detail page inconsistency: preventing inconsistent item identities, and improving the customer experience by automatically detecting and creating factual relationships between ASINs: e.g. variation families, newer versions, 3) reducing selling partner listing friction: reducing GTIN defects in the catalog, and false conflicts in contributions, and 4) improving brand customer experience: providing a strong brand identity to contributions and ASINs, by matching them to Universal Brand Catalog brand entities.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
PREFERRED QUALIFICATIONS
- PhD
- 2+ years of CS, CE, ML or related field experience
- Have publications at top-tier peer-reviewed conferences or journals
- Proven track record of successfully applying ML-based solutions to complex problems in business, science, or engineering.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
#J-18808-Ljbffr