Library Scientist

Miratech

. Analyse the current knowledge base content and structure, including volume, format, ownership, age, duplication, structural consistency, and tagging practice .

Posted 10/9/2026full-timeRemote • CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in taxonomy design, metadata modeling, and information architecture to enhance content findability and AI retrieval quality. Proficient in conducting content analysis and search behavior evaluation to inform governance and optimization strategies.

Highest-signal resume keywords

Master Of Library And Information Science (MLIS/MISt)Taxonomy DesignMetadata ModelingEnterprise Search Behaviour AnalysisContent Governance Standards

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Content AnalysisControlled VocabulariesInformation ArchitectureSearch Configuration TuningRelevance TuningSemantic Search ConfigurationDocumenting FindingsAI Retrieval Quality EvaluationArticle Granularity DefinitionMetadata Schema Definition

Soft Skills

Clear CommunicationCollaboration

Tools & Technologies

Genesys CloudSalesforce KnowledgeServiceNow Knowledge ManagementZendesk Guide

Certifications & Qualifications

Government Of Canada Security Clearance

Industry Keywords

SKOSDublin CoreISO 25964Schema.orgWCAG Accessibility RequirementsRecords ManagementKnowledge-Centred Service (KCS)Bilingual English/French

Tech Stack

Tools & technologies

CloudServiceNow

About the role

Key responsibilities & impact

Analyse the current knowledge base content and structure, including volume, format, ownership, age, duplication, structural consistency, and tagging practice
Document current-state structural problems suppressing findability
Conduct a baseline content analysis of 100–200 pilot articles and produce scored findings and a remediation profile
Analyse search logs and agent/self-service search behaviour, including query volume, top queries, zero-result and low-result queries, abandonment, refinement patterns, and vocabulary gaps
Build a taxonomy blueprint for AI retrieval, including hierarchy, facets, controlled vocabularies, synonym rings, and placement and relationship rules
Validate the taxonomy with business-line subject matter experts and real queries
Structure content for machine consumption by defining article granularity, chunking, heading structure, answer-first writing patterns, and disambiguation rules
Define the metadata schema, value sets, inheritance rules, audience and jurisdiction markers, lifecycle and review-date fields, and tagging conventions
Specify and test keyword and semantic search configuration, including relevance tuning, synonym and acronym handling, stemming, boosting, and result presentation
Establish baseline search-performance measures, target improvements, and AI answer-quality evaluation measures
Establish content governance standards, lifecycle and review cadence, ownership, and publication quality criteria
Work with the Senior Business Consultant to align the Governance Playbook and taxonomy
Attend proofs of concept and test Genesys Cloud tagging, faceted search, multi-audience content, and AI retrieval accuracy and attribution
Document the model for the client’s content team and run knowledge-transfer working sessions

Requirements

What you’ll need

Master of Library and Information Science (MLIS/MISt) or an equivalent information science qualification; this is a genuine requirement
5+ years applying information science in an enterprise or digital context, including taxonomy design, metadata modelling, controlled vocabularies, information architecture, or enterprise search
Demonstrated experience designing and implementing a taxonomy and metadata model for a production content environment, with examples of deliverables produced
Practical experience with enterprise search behaviour analysis, including search logs, zero-result diagnosis, relevance failure diagnosis, and search configuration tuning
Working knowledge of taxonomy and metadata standards, including SKOS, Dublin Core, ISO 25964, and schema.org
Understanding of how information architecture affects AI and natural-language retrieval quality
Ability to conduct structured, scored article-level content analysis and report findings clearly to a non-specialist audience
Eligible to obtain and hold a Government of Canada security clearance
Nice to have: hands-on experience with Genesys Cloud knowledge base and authoring/optimisation tooling, or comparable platforms such as Salesforce Knowledge, ServiceNow Knowledge Management, or Zendesk Guide
Nice to have: experience preparing content corpora for AI, retrieval-augmented generation, or virtual agent consumption
Nice to have: experience establishing AI answer-quality evaluation
Nice to have: government, public sector, or academic library background, including plain-language standards, WCAG accessibility requirements, and records management obligations
Nice to have: bilingual English/French, including bilingual taxonomy and parallel content-set management
Nice to have: experience with KCS (Knowledge-Centred Service) methodology

Benefits

Comp & perks

Comprehensive compensation and benefits package
Health insurance
Language courses
Relocation program
Professional development opportunities
Certification programs
Mentorship and talent investment programs
Internal mobility opportunities
Internship opportunities
Collaboration on impactful projects for top global clients
Inclusive and supportive work environment with open communication
Regular team-building company social events
Sustainable business practices focused on IT education, community empowerment, fair operating practices, environmental sustainability, and gender equality