Key Responsibilities and Required Skills for Knowledge Research Specialist

🎯 Role Definition

The Knowledge Research Specialist is responsible for locating, synthesizing, and managing internal and external knowledge to accelerate decision-making across the organization. Using advanced search techniques, systematic literature reviews, primary research (interviews, surveys), and quantitative analysis, the Specialist transforms raw information into structured, reusable assets: annotated bibliographies, knowledge bases, taxonomies, briefs, and visualizations. This role partners with product, engineering, policy, marketing, and business teams to ensure insights are discoverable, accurate, and actionable.

Key responsibilities emphasize information retrieval, evidence synthesis, content curation, taxonomy and ontology development, knowledge graph and metadata management, and the ability to present findings to technical and non-technical audiences. The ideal candidate understands modern knowledge-management platforms, research best practices, and fundamentals of data science and NLP for automated extraction and classification.

📈 Career Progression

Typical Career Path

Entry Point From:

Research Assistant / Research Coordinator
Knowledge Management Coordinator / Content Curator
Market Research Analyst or Business Analyst

Advancement To:

Senior Knowledge Research Specialist / Lead Researcher
Knowledge Manager or Head of Knowledge
Research Director, Competitive Intelligence Lead, or Product Insights Lead

Lateral Moves:

Information Architect
UX Researcher
Competitive Intelligence Analyst

Core Responsibilities

Primary Functions

Conduct comprehensive literature reviews, systematic evidence syntheses, and environmental scans using academic databases, grey literature sources, industry reports, patent databases, and web archives to produce rigorous, up-to-date dossiers that inform strategy and product decisions.
Design and execute mixed-methods research studies (qualitative interviews, focus groups, structured surveys, and quantitative data analysis) to answer stakeholder questions and validate hypotheses; draft protocols, recruit participants, and manage IRB or ethical reviews where applicable.
Create concise, executive-ready summaries, research briefs, policy memos, and slide decks that translate complex findings into clear recommendations and prioritized actions for executives and cross-functional teams.
Build and maintain centralized knowledge repositories and searchable content hubs (Confluence, SharePoint, Notion, or custom knowledge bases) with consistent metadata, tagging conventions, and access controls to maximize discoverability and reuse of research outputs.
Develop and govern taxonomies, controlled vocabularies, and ontologies aligned to business domains; implement metadata schemas and content models that support semantic search and interoperability across systems.
Architect and maintain knowledge graphs and entity relationships to surface connections across documents, experts, projects, and data sets; partner with engineers to operationalize graph databases and schema.
Design and implement information retrieval strategies, advanced search queries, and boolean logic optimized for diverse data sources; iterate search strategies to improve precision and recall for internal stakeholders.
Apply text-mining, NLP, and information-extraction techniques (topic modeling, named entity recognition, summarization) to scale synthesis across large corpora and support automated tagging, classification, and alerting.
Perform quantitative analysis using SQL, Python, R, Excel, or BI tools (Tableau, Power BI, Looker) to identify trends, calculate metrics, and validate insights drawn from internal usage logs, surveys, or external datasets.
Curate, annotate, and enrich content with provenance, methodology notes, and confidence levels to ensure transparency and traceability of evidence used in decision-making.
Coordinate and run knowledge-sharing forums, brown-bag sessions, and cross-functional workshops; synthesize learnings and capture institutional memory from community discussions and retrospectives.
Establish and monitor knowledge KPIs (search success rate, time-to-insight, reuse metrics) and produce regular analytics reports to inform continuous improvement of research workflows and knowledge products.
Create and enforce standards, templates, and playbooks for research deliverables, tagging, version control, and archival processes to ensure consistency and quality across teams.
Manage research project timelines, budgets, and vendor relationships for commissioned studies and external data purchases; track milestones and deliverables through project management tools.
Conduct subject-matter expert mapping and maintain an expert directory; facilitate expert interviews and advisory sessions to fill knowledge gaps and accelerate domain learning.
Execute competitive intelligence and market landscape analyses — tracking competitors, technology trends, regulatory changes, and market signals — and generate alerting systems for high-priority developments.
Produce reproducible research packages and data artifacts (code notebooks, datasets, dashboards) to enable handoffs to engineering and analytics teams for operationalization.
Implement quality control protocols for data collection and synthesis (double coding, inter-rater reliability checks, audit trails) to maintain robustness and defensibility of findings.
Ensure compliance with data governance, privacy, and intellectual property policies when ingesting, storing, and sharing content; coordinate with legal and compliance teams for licensing or sensitive data handling.
Drive stakeholder intake and triage processes to prioritize research requests, manage expectations, and align outputs with business impact and resource availability.
Mentor junior researchers and knowledge curators, providing guidance on research methods, synthesis best practices, and knowledge management workflows to raise team capability.
Maintain an active scanning and learning routine for new methodologies, tools, and domain developments (NLP, knowledge graphs, taxonomies) and recommend technology investments to scale knowledge operations.
Translate research findings into product requirements and user stories for engineering and design teams; collaborate on prototypes to validate discoveries and measure user impact.
Facilitate cross-team alignment by mapping research findings to strategic objectives, OKRs, and roadmaps; prepare materials for leadership reviews and decision-making forums.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Provide subject-matter expertise to product, marketing, sales, and policy teams on emerging topics and knowledge assets.
Assist in onboarding and training programs to teach teams how to search, interpret, and contribute to the knowledge base.
Run periodic audits of knowledge coverage and identify gaps, duplication, or outdated materials for remediation.
Prepare procurement briefs and specifications for external research vendors, data providers, or tools.

Required Skills & Competencies

Hard Skills (Technical)

Systematic literature review and evidence synthesis — experience designing, executing, and documenting rigorous review protocols across academic and industry sources.
Information retrieval and advanced search — proficiency with boolean logic, indexing, and search optimization across multiple repositories and databases.
Taxonomy and ontology design — hands-on experience building and maintaining controlled vocabularies, schemas, and entity models to enable semantic search.
Knowledge graph and semantic modeling — familiarity with graph databases, RDF/OWL concepts, or practical experience building entity relationships for discovery.
NLP and text analytics — applied experience with topic modeling, NER, text classification, summarization, or using libraries/tools such as spaCy, NLTK, Hugging Face, or similar.
Data analysis and visualization — strong SQL and experience with Python or R for data manipulation; ability to create dashboards and visualizations in Tableau, Power BI, or Looker.
Research methods — qualitative and quantitative research design, interview facilitation, survey design, sampling, and statistical basics for analysis and hypothesis testing.
Metadata management & CMS — experience with metadata schemas, content management systems (Confluence, SharePoint, Notion, Drupal) and tagging strategies.
Documentation and reproducible research — ability to produce reproducible artifacts (Jupyter notebooks, RMarkdown), version control (Git), and maintain provenance of sources.
Knowledge management platforms & search tech — practical knowledge of enterprise search tools (ElasticSearch, Solr), knowledge base platforms, and integration patterns.
Project management tools and practices — experience with Jira, Asana, Trello, or similar to manage research projects and stakeholder requests.
Legal and ethical data handling — understanding of privacy, IP/licensing, and ethical considerations when collecting, storing, and publishing research outputs.

Soft Skills

Clear, persuasive communication — ability to distill complex evidence into concise executive summaries and present to diverse stakeholders.
Critical thinking and synthesis — aptitude for identifying signal from noise, triangulating sources, and assessing credibility and bias.
Stakeholder management — experience prioritizing requests, negotiating scope, and building relationships with product, policy, and business leads.
Curiosity and continuous learning — proactive in scanning new research methods, domain trends, and tools to improve knowledge operations.
Attention to detail and quality orientation — rigorous approach to documentation, citations, and reproducible workflows.
Collaboration and facilitation — skilled at running workshops, interviews, and cross-functional sessions to co-create insights and outcomes.
Time management and prioritization — ability to balance multiple requests, deadlines, and shifting priorities in a fast-paced environment.
Coaching and mentorship — experience supporting junior researchers and knowledge contributors to elevate team capability and standards.
Problem-solving and adaptability — comfortable working with ambiguous questions and designing iterative research approaches to answer them.
Influence without authority — ability to advocate for knowledge practices and governance across organizational boundaries.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Information Science, Library Science, Data Science, Social Sciences, Public Policy, or related field.

Preferred Education:

Master’s degree or PhD in Information Science, Library & Information Studies, Data Science, Cognitive Science, Public Policy, or a domain-relevant discipline.

Relevant Fields of Study:

Information Science / Knowledge Management
Library & Information Studies
Data Science, Statistics, or Applied Analytics
Social Sciences (Sociology, Anthropology, Political Science)
Public Policy, Economics, Business, or Domain-specific disciplines (Healthcare, Energy, Technology)

Experience Requirements

Typical Experience Range:

3–7 years of progressively responsible experience in research, knowledge management, competitive intelligence, or a related analytical role.

Preferred:

5+ years of direct experience conducting literature reviews, building taxonomies/ontologies, and managing enterprise knowledge systems; demonstrated experience applying NLP/text analytics and delivering insights to leadership; experience in regulated industries (healthcare, finance, government) is a strong plus.