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The vision of the Product Knowledge Ontology Team is to provide a standardized, semantically rich, easily discoverable, extensible, and universally applicable body of product knowledge that can be consistently utilized across customer shopping experiences, selling partner listing experiences and internal enrichment of product data. We aim to make product knowledge compelling, easy to use, and feature rich. Our work to build comprehensive product knowledge allows us to semantically understand a customer’s intent – whether that is a shopping mission or a seller offering products. We strive to make these experiences more intuitive for all customers.
As an Ontologist, you work on a global team of knowledge builders to deliver world-class, intuitive, and comprehensive taxonomy and ontology models to optimize product discovery for Amazon web and mobile experiences. You collaborate with business partners, data science and engineering teams to deliver knowledge-based solutions to enable product discoverability for customers. In this role, you will directly impact the customer experience as well as the company’s product knowledge foundation.
- Key job responsibilities
- Develop logical, semantically rich, and extensible data models for Amazon's expansive product catalog
- Ensure our ontologies provide comprehensive sub-domain coverage that are available for machine ingestion and inference
- Research worldwide understanding of Amazon products to develop scalable data models that solve customer problems and enhance product discoverability
- Create new schema using Generative Artificial Intelligence (generative AI) models
- Expand existing data retrieval techniques by utilizing our extensive product catalog and query languages
- Analyze website metrics and product discovery behaviors to make data-driven decisions on optimizing our knowledge graph data models globally
- Contribute to the development of new tool features and processes for the Ontology team
- Drive alignment to ontology modeling and technology best practices and principles within our organization and across Amazon
- Coordinate cross-functional projects with a broad range of technical and non-technical teams
About the team
Our team has a global presence: we celebrate diverse cultures and backgrounds within our team and our customer base. We are committed to furthering our culture of inclusion, offering continuous access to internal affinity groups as well as highlighting diversity programs. We care about your career growth, from your initial company introduction and training sessions, to continuous support throughout your entire career at Amazon. We recognize each team member as an individual, and we will build on your skills to help you grow. We have a broad mix of experience levels and tenures, and we are building an environment that celebrates knowledge sharing.
Basic Qualifications
- Experience in data retrieval techniques
- Experience in written and spoken English and an ability to compose grammatically correct, concise and accurate written responses
- Degree in Library Science, Information Systems, Linguistics or equivalent professional experience
- 3+ years of relevant work experience working in ontology and/or taxonomy roles
- Ability to quickly understand complex processes and communicate them in simple language
- Ability to communicate knowledge-based requirements and needs to engineering and retail teams
- Familiarity with Semantic Web technologies (RDF/s, OWL), query languages (SPARQL) and validation/reasoning standards (SHACL, SPIN)
- Knowledge of open-source and commercial ontology engineering editors (e.g. Protege, Top Quadrant products, Pool Party)
- Detail-oriented problem-solving, ability to work in fast-changing environment and manage ambiguity
- Proven track record of strong communication and interpersonal skills
Preferred Qualifications
- Experience building ontologies in the e-commerce and semantic search spaces
- Experience working with schema-level constructs like higher-order classes, punning or property inheritance
- Knowledge of software engineering life cycle
- Master’s degree in Library Science, Information Systems, Linguistics or other relevant fields
- Proficiency in SQL, SPARQL
- Exposure to data science and/or machine learning, including graph embeddings
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 onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 82,700.00 - 144,700.00 USD annually
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About Amazon

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
Employees
Seattle
Headquarters
Reviews
2.9
10 reviews
Work Life Balance
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Compensation
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Culture
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2.3
Management
2.1
35%
Recommend to a Friend
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Flexible scheduling options
Cons
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Limited growth and promotion opportunities
High stress and demanding work environment
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2 data points
L2
L3
L4
L5
L6
L2 · Data Analyst L2
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Experience
Positive 10%
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Application Review
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Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
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Team Matching
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