Pearson Taxonomist in United States



At Pearson, we’re committed to a world that’s always learning and to our talented team who makes it all possible. From bringing lectures vividly to life to turning textbooks into laptop lessons, we are always re-examining the way people learn best, whether it’s one child in our own backyard or an education community across the globe.We are bold thinkers and standout innovators who motivate each other to explore new frontiers in an environment that supports and inspires us to always be better. By pushing the boundaries of technology — and each other to surpass these boundaries — we create seeds of learning that become the catalyst for the world’s innovations, personal and global, large and small.

TheTaxonomist will report to the Sr Product Manager for Content Metadata Enrichment within the Personalized Learning and Analytics Group. They will provide essential subject matter expertise globally to develop and implement strategies, standards, and guidelines for taxonomies at Pearson. The Taxonomist will drive for standardization of specifications and processes. The role will work across lines of business and closely with product, engineering, learning design, personalized learning, and analytics teams.

Specific responsibilities include:

  • Work with cross-functional, cross-business teams to govern the creation, implementation, and management of taxonomies, ontologies and controlled vocabularies using best practices, and to establish scalable workflows around their use.

  • Work with engineering teams to vet and recommend new technologies that may enhance taxonomy management capabilities

  • Work under the supervision of the Sr. Product Manager for Content Metadata Enrichment to ensure that all taxonomies and related workflows and tools promote the enterprise metadata enrichment strategy

  • Deliver documentation, advocacy, and hands-on training materials related to the development and use of taxonomies and related tools

  • Establish Pool Party as the single source for metadata specification and management at Pearson

  • Own and manage use of the Pool Party platform

  • Conduct audits and point to areas across the business that would benefit from further taxonomy development or management and suggest viable solutions


Applicants should be familiar with technologies and industry standards around taxonomy management.

The ideal candidate will be able to shift from detailed and technical conversations related to areas such as knowledge graphs to more strategic conversations such as the foundational importance of standardized controlled vocabularies.

This role will require the ability to adapt within a rapidly evolving organization, and applicant should be excited (rather than intimidated) about the notion of building collaboration against a backdrop of broad organizational (and industry) change.

Experience/Skills Required:

  • Undergraduate degree required; relevant graduate degree preferred. High GPA expected.

  • 5 years of experience in a role related to taxonomy/ontology management, preferably with experience in product development.

  • Experience with the Pool Party semantic technology platform.

  • Ideally, some experience in publishing and digital learning.

  • Familiarity and experience with Linked Data and RDF data modeling (and knowledge representation languages such as OWL).

  • Excellent written and verbal communication skills.

  • Able to work both on a team and independently to deliver results.

  • Comfortable working in a fast-paced, dynamic environment.


Pearson is an Equal Opportunity and Affirmative Action Employer and a member of E-Verify. All qualified applicants, including minorities, women, protected veterans, and individuals with disabilities are encouraged to apply.

Primary Location: US-NJ-Hoboken

Other Locations US-Remote

Work Locations: US-NJ-Hoboken-221 River 221 River Street Hoboken 07030

Job: Technology

Organization: Global Product

Employee Status: Regular Employee

Job Type: Standard

Shift: Day Job

Job Posting: Jun 27, 2017

Req ID: 1710993