Lead Specialist, AI Scientist

We are seeking a Lead Specialist, AI Data & Knowledge Architect , to design and build the knowledge foundation of Pearson’s next generation of intelligent K–12 assessment and learning systems.

This role sits within the Measurement & Learning team and operates at the intersection of Content Development, Learning Science, Technology, AI Science, and Psychometrics, with responsibility for translating deep assessment and measurement expertise into extensible computational representations. Prior assessment experience is helpful but not required.

This work will build a deep, layered knowledge architecture of interconnected graphs beneath our assessment authoring system. It will ground the system's AI agents and intelligent workflows, and support both formative and summative assessment. Standards, competencies, claims, evidence, task designs, scoring, and content will be represented as connected, machine-readable knowledge that AI, measurement, and content workflows can reason over.

This is a founding, hands-on role for someone eager to innovate at the edge of current practice: building what does not yet exist, testing it rigorously, and adapting quickly. The architecture will support predictive, statistical, graph-based, deterministic, and generative AI methods alike.

• Design and build the knowledge graph and underlying knowledge model for our assessment and learning domains, selecting among ontologies, schemas, vocabularies, and taxonomies as the problem requires
• Represent standards, competencies, skills, claims, evidence, task characteristics, misconceptions, content, and their relationships as computationally usable structures
• Design for extensibility across content areas, programs, assessment contexts, and future AI capabilities, starting simple without closing off future growth
• Establish approaches for stable identity, versioning, provenance, lineage, and lifecycle status of knowledge objects
• Clearly separate authoritative knowledge, expert judgments, derived information, and machine-generated hypotheses
• Evaluate and select graph and semantic technologies based on extensibility, interoperability, reasoning needs, maintainability, and downstream AI use
• Build the pipelines, mappings, and synchronization processes that connect item banks, standards, metadata, psychometric data, and other sources into a coherent knowledge layer
• Develop the schemas, APIs, data contracts, and services through which applications, agents, and analytic workflows access the knowledge architecture
• Partner with software engineers to integrate the knowledge layer into our assessment authoring system

Knowledge-Grounded AI, Retrieval & Context

• Design retrieval and context architectures that let AI and machine-learning systems use structured knowledge effectively, including graph, semantic, vector, and hybrid retrieval
• Create task-specific knowledge projections so each capability receives what it needs without complex relationships being flattened into unstructured prompts
• Maintain traceability between AI outputs and the knowledge used to produce them
• Work with Applied AI Systems Engineers to ensure generative, predictive, agentic, and analytic capabilities operate reliably over the shared knowledge layer

Innovation & Technical Judgment

• Exercise a high degree of autonomy in shaping architecture where no established blueprint exists
• Prototype quickly, test assumptions empirically, and change course when evidence warrants
• Evaluate emerging approaches, including graph-based, knowledge-grounded, and neuro-symbolic methods, and adopt those that show measurable value
• Make architectural decisions, assumptions, and tradeoffs explicit and open to challenge from colleagues

Responsible AI, Quality & Evaluation

• Design knowledge systems that make AI…