United States onlyThe Specialist, Researcher, also know in the industry as Specialist, Applied Research Scientist, Measurement & Learning Systems supports the research agenda across our formative assessment portfolio, while contributing to coherent assessment system research and innovative applied research initiatives spanning formative, screening/progress monitoring, interim, summative, and custom assessment contexts.
Reporting to the Lead, Formative Product Measurement, this role contributes to research and validation efforts related to efficacy studies, learner and domain modeling, recommender systems, predictive validity, implementation research, case studies, AI validation studies, and innovative applied research initiatives supporting learning and measurement systems across assessment contexts.
This role supports research and validation activities that advance student learning outcomes through product innovation, AI-assisted measurement, and continuous improvement.
This position operates within a research-forward, cross-functional product environment and partners closely with psychometrics, product management, learning science, AI science, content, implementation, and technology teams. The role contributes to innovative research and validation methodologies supporting formative assessment, learner modeling, AI-assisted systems, instructional guidance, and broader assessment innovation initiatives.
Key Responsibilities
1. Applied Research & Validation Studies
Support the design, execution, and interpretation of applied research studies related to learning, assessment systems, and student outcomes.
Conduct efficacy, implementation, predictive validity, evaluation, and innovative applied research studies aligned with product and organizational priorities.
Contribute to studies examining instructional impact, engagement, assessment-as-learning outcomes, learner growth, and educational innovation.
Support development of case studies and evidence narratives demonstrating product implementation, impact, and innovation outcomes.
Support research examining coherence, alignment, and evidence continuity across formative, screening/progress monitoring, interim, summative, and custom assessment systems.
Translate research findings into actionable recommendations that inform product and custom design, AI-assisted workflows, reporting systems, learner guidance, and instructional experiences.
Contribute to innovative methodologies and analytical approaches supporting educational product research, learning systems, and measurement validation.
2. Learner Modeling & Recommendation Research
Support research related to learner models, domain models, recommender systems, adaptive feedback loops, and learning guidance systems.
Contribute to investigations examining how assessment evidence can inform instructional guidance and personalized learning pathways.
Collaborate with cross-functional teams to evaluate and refine recommendation approaches, learning insight frameworks, and next-generation formative learning systems.
Support research and analytical activities across large-scale item banks, dynamic assessment ecosystems, and integrated learning and measurement environments.
3. AI Validation & Emerging Research Applications
Support validation studies related to AI-assisted assessment and learning workflows, including AI-assisted item generation, AI-enabled insights, and generative AI applications.
Contribute to frameworks evaluating quality, alignment, validity, fairness, traceability, and educational usefulness of AI-assisted outputs.
Partner with AI Science, Content, Learning Science, Technology, and Measurement teams to evaluate emerging AI-enabled capabilities across learning and assessment systems.
Explore emerging approaches supporting personalized learning, adaptive guidance, generative AI applications, and next-generation assessment and learning systems.
Stay informed on emerging research, methodological developments, and innovation trends related to AI in educational and learning contexts.
4. Technical Documentation, Dissemination & Thought Leadership
Contribute to technical documentation supporting product validity, efficacy, interpretive, and measurement claims.
Develop research summaries, technical reports, conference proposals, presentations, manuscripts, and dissemination materials.
Translate research findings into clear, actionable insights for technical and non-technical audiences.
Support external publications, conference presentations, and enterprise thought leadership initiatives.
Support development of research-informed evidence narratives and technical documentation supporting interpretation, innovation initiatives, and commercialization efforts.
5. Cross-Functional Collaboration
Collaborate with Psychometrics, Product Management, Learning Science, Content, AI Science, Technology, Implementation, and Commercialization teams on research priorities and studies.
Support research planning, study coordination, data interpretation, and evidence generation across initiatives.
Contribute methodological insight to product, innovation, AI-assisted workflow, and assessment system discussions.
Help ensure research and evidence generation align with intended uses, learner impact goals, and responsible innovation practices.
6. Additional Measurement & Research Support
Support targeted psychometric, analytical, or methodological investigations related to products, assessment systems, and research initiatives, as needed.
Contribute to exploratory analyses, validation investigations, and special studies supporting product evolution and innovation.
Uphold principles aligned with the Standards for Educational and Psychological Testing and responsible AI use.
Qualifications
Required
PhD or advanced degree in Educational Measurement, Psychometrics, Quantitative Psychology, Statistics, Educational Data Mining, Learning Analytics, Machine Learning, Learning Sciences, or a related field.
Experience conducting applied educational, learning, assessment, or product research studies.
Strong understanding of research design, statistical analysis, validation methodologies, and interpretation.
Experience analyzing educational, learner, behavioral, or assessment-related data.
Strong written and verbal communication skills, including technical writing, dissemination, and presentation development.
Experience collaborating in cross-functional product, innovation, or research environments.
Preferred
Experience supporting efficacy, implementation, predictive validity, evaluation, or innovation-focused research studies.
Familiarity with formative, screening/progress monitoring, interim, summative, or custom assessment systems; learner models; recommender systems; adaptive learning technologies; or instructional guidance systems.
Experience evaluating or validating AI-assisted or generative AI educational technologies.
Familiarity with psychometric concepts and educational measurement principles.
Experience with analytical, statistical, or machine learning tools such as R, Python, SQL, SAS, or related environments.
Experience contributing to technical reports, peer-reviewed publications, conference presentations, or thought leadership artifacts.
Additional Consideration
Candidates with strong expertise in applied educational research, learning analytics, educational data mining, AI-enabled learning systems, or advanced analytical methods are encouraged to apply, even if their background is not rooted in traditional psychometrics.
Professional Profile
Research-oriented and intellectually curious.
Thinks critically and systemically about learning, measurement, educational innovation, and coherent assessment ecosystems.
Balances scientific rigor with applied product and learner impact.
Translates complex findings into practical and actionable insights.
Communicates clearly across technical and non-technical audiences.
Collaborates effectively across disciplines and functions.
Effectively leverages AI-assisted tools and emerging technologies to accelerate research, analysis, innovation, and knowledge synthesis while maintaining methodological rigor and critical evaluation.
Believes assessment can support learning, not just measure it.
Compensation at Pearson is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific location. As required by the California, Colorado, Hawaii, Illinois, Maryland, Minnesota, New Jersey, New York State, New York City, Vermont, Washington State, and Washington DC laws, the pay range for this position is as follows:
The minimum full-time salary range is between $100,000 - $125,000.
This position is eligible to participate in an annual incentive program, and information on benefits offered is here.
Applications will be accepted through Friday, May 22nd, 2026. This window may be extended depending on business needs.
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