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Senior Ontologist, Product Knowledge

Amazon Development Center Germany GmbH

München

Vor Ort

EUR 70.000 - 90.000

Vollzeit

Vor 4 Tagen
Sei unter den ersten Bewerbenden

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Zusammenfassung

A leading company is seeking an Ontologist to join their global team. This role involves developing comprehensive taxonomy and ontology models to enhance product discoverability. Candidates should have a degree in Library Science or related fields, and extensive experience in ontology and taxonomy roles. The position emphasizes collaboration with business partners and engineering teams, aiming to optimize customer experiences through innovative data models.

Qualifikationen

  • 5+ years of relevant work experience in ontology and/or taxonomy roles.
  • Proven skills in data retrieval and data research techniques.

Aufgaben

  • Develop logical, semantically rich data models for Amazon's product catalog.
  • Analyze website metrics to optimize knowledge graph data models.

Kenntnisse

Data Retrieval
Communication
Problem Solving
Detail-oriented
Interpersonal Skills

Ausbildung

Degree in Library Science
Master’s degree in Library Science

Tools

Protege
TopQuadrant products
PoolParty
SQL
SPARQL

Jobbeschreibung

Job Location : Munich (this is not a remote opportunity).

The vision of the Ontology Product Knowledge 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 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.

Tasks and Responsibilities :

  • Develop logical, semantically rich, and extensible data models for Amazon's extensive product catalog
  • Ensure our ontologies provide comprehensive domain coverage that are available for both human and machine ingestion and inference
  • Create new schema using Generative Artificial Intelligence (generative AI) models
  • Analyze website metrics and product discovery behaviors to make data-driven decisions on optimizing our knowledge graph data models globally
  • Expand and refine the expansion of data retrieval techniques to utilize our extensive knowledge graph
  • Contribute to team goal setting and future state vision
  • Drive and coordinate cross-functional projects with a broad range of merchandisers, engineers, designers, and other groups that may include architecting new data solutions
  • Develop team operational excellence programs, data quality initiatives and process simplifications
  • Evangelize ontology and semantic technologies within and across teams at Amazon
  • Develop and refine data governance and processes used by global Ontologists
  • Mentor and influence peers

Inclusive Team Culture : 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.

Work / Life Harmony : Our team believes that striking the right balance between work and your outside life is key. Our work is not removed from everyday life, but instead is influenced by it. We offer flexibility in working hours and will work with you to facilitate your own balance between your work and personal life.

Career Growth : Our team cares 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

  • Degree in Library Science, Information Systems, Linguistics or equivalent professional experience
  • 5+ years of relevant work experience working in ontology and / or taxonomy roles
  • Proven skills in data retrieval and data research techniques
  • Ability to quickly understand complex processes and communicate them in simple language
  • Experience creating and communicating technical requirements to engineering teams
  • Ability to communicate to senior leadership (Director and VP levels)
  • Experience with generative AI (e.g. creating prompts)
  • Knowledge of 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, TopQuadrant products, PoolParty)
  • Detail-oriented problem solver who is able to work in fast-changing environment and manage ambiguity
  • Proven track record of strong communication and interpersonal skills
  • Proficient English language skills

PREFERRED QUALIFICATIONS

  • Master’s degree in Library Science, Information Systems, Linguistics or other relevant fields
  • Experience building ontologies in the e-commerce and semantic search spaces
  • Experience working with schema-level constructs (e.g. higher-level classes, punning, property inheritance)
  • Proficiency in SQL, SPARQL
  • Familiarity with software engineering life cycle
  • Familiarity with ontology manipulation programming libraries
  • Exposure to data science and / or machine learning, including graph embedding
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