{"id":3729770,"url":"https://alion.io/job/pearson-lead-specialist-ai-scientist-4","title":"Lead Specialist, AI Scientist","company":{"id":34029,"name":"Pearson","domain":"pearson.com","url":"https://alion.io/company/pearson","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":81,"open_postings":25,"ghost_share":0,"stale_share":0.76,"repost_share":0,"time_to_fill_p50_days":26,"computed_at":"2026-11-06T05:58:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":140000,"max":160000,"currency":"USD","period":"year","gross":null,"usd_annual":160000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Embeddings","optional":true}],"status":"live","first_seen_at":"2026-11-04T04:50:40Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-06T15:15:30Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Role Overview\nWe 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.\nThis 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.\nThis 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.\nThis 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.\nKnowledge Architecture & Modeling\nDesign 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\nRepresent standards, competencies, skills, claims, evidence, task characteristics, misconceptions, content, and their relationships as computationally usable structures\nDesign for extensibility across content areas, programs, assessment contexts, and future AI capabilities, starting simple without closing off future growth\nEstablish approaches for stable identity, versioning, provenance, lineage, and lifecycle status of knowledge objects\nClearly separate authoritative knowledge, expert judgments, derived information, and machine-generated hypotheses\nGraph & AI Data Architecture\nEvaluate and select graph and semantic technologies based on extensibility, interoperability, reasoning needs, maintainability, and downstream AI use\nBuild the pipelines, mappings, and synchronization processes that connect item banks, standards, metadata, psychometric data, and other sources into a coherent knowledge layer\nDevelop the schemas, APIs, data contracts, and services through which applications, agents, and analytic workflows access the knowledge architecture\nPartner with software engineers to integrate the knowledge layer into our assessment authoring system\nKnowledge-Grounded AI, Retrieval & Context\nDesign retrieval and context architectures that let AI and machine-learning systems use structured knowledge effectively, including graph, semantic, vector, and hybrid retrieval\nCreate task-specific knowledge projections so each capability receives what it needs without complex relationships being flattened into unstructured prompts\nMaintain traceability between AI outputs and the knowledge used to produce them\nWork with Applied AI Systems Engineers to ensure generative, predictive, agentic, and analytic capabilities operate reliably over the shared knowledge layer\nInnovation & Technical Judgment\nExercise a high degree of autonomy in shaping architecture where no established blueprint exists\nPrototype quickly, test assumptions empirically, and change course when evidence warrants\nEvaluate emerging approaches, including graph-based, knowledge-grounded, and neuro-symbolic methods, and adopt those that show measurable value\nMake architectural decisions, assumptions, and tradeoffs explicit and open to challenge from colleagues\nResponsible AI, Quality & Evaluation\nDesign knowledge systems that make AI reasoning transparent, auditable, and traceable to its sources\nSupport AI that strengthens human expertise rather than replacing it, with clear points for expert review and judgment\nPartner with AI engineers, psychometricians, and content experts to test whether knowledge and retrieval designs measurably improve accuracy and reliability\nEstablish quality, coverage, and consistency measures for knowledge assets\nUphold responsible data practices, including privacy, security, access control, and appropriate use of student and content data\nCross-Functional Collaboration\nPartner with Technology teams and software engineers responsible for the assessment authoring system\nCo-design architecture with AI Science colleagues and Applied AI Systems Engineers, providing and receiving technical challenge\nPartner with psychometricians and learning scientists to represent measurement and learning concepts faithfully\nWork with content development teams and subject-matter experts to capture domain knowledge, terminology, relationships, and constraints\nCommunicate complex architectural concepts clearly to technical and non-technical audiences\nRequired Qualifications\nAdvanced degree in Computer Science, Artificial Intelligence, Information Science, Knowledge Engineering, or a related field, or equivalent experience\nDemonstrated experience designing and building knowledge graphs in production or substantial applied settings\nHands-on experience with knowledge modeling and semantic schema design, using approaches such as ontologies, property-graph schemas, vocabularies, and taxonomies\nStrong command of graph and semantic technologies (e.g., RDF, OWL, SHACL, SPARQL, property graphs) and their architectural tradeoffs\nStrong data architecture skills, including data modeling, integration pipelines, identity, versioning, provenance, and lineage\nStrong programming ability in Python or comparable languages, with a record of building working systems and APIs\nDeep understanding of predictive and generative AI, including how retrieval-grounded and agentic systems use structured knowledge\nTrack record of building novel systems where no established solution existed, with sound judgment and high autonomy\nAbility to translate expertise from other disciplines into computational representations and to communicate tradeoffs clearly.\nPreferred Qualifications\nStrong understanding of how educational knowledge is structured, through K-12 standards, competency frameworks, or skills-based architectures such as skills taxonomies and credential frameworks, ideally from modeling them as structured data\nExperience with knowledge graphs that power applications such as content generation, framework alignment, personalized guidance, or analytic insights\nExperience with formal ontology engineering, including OWL-based modeling, reasoning, or validation\nExperience building hybrid AI systems that combine machine-learned or generative models with structured knowledge, rules, or constraints, including neuro-symbolic approaches\nExperience with graph machine learning, graph embeddings, link prediction, or graph neural networks\nExperience in domains where outputs must be validated against reliable evidence or expert judgment, such as scientific, healthcare, financial, or legal AI\nExperience with evaluation datasets, human annotation and adjudication workflows, or benchmark development\nFamiliarity with educational measurement, psychometrics, principled assessment design, or assessment content systems\nPublications, patents, or open-source contributions in knowledge representation or graph technologies\nDesign Philosophy\nKnowledge as a durable, extensible organizational asset\nBuild to learn: prototype, measure, iterate\nInnovation at the edge of current practice, grounded in evidence\nTransparent, auditable, and traceable AI reasoning\nAI that supports rather than supplants human expertise\nThe right method for the problem, not the newest one\nWhy This Role Matters\nThis role lays the foundation for an industry-defining assessment system. By making expert knowledge explicit, extensible, and computationally usable, and by inventing new approaches where none exist, this architect will enable more accurate, transparent, and trustworthy generation, alignment, verification, analysis, and guidance across formative and summative assessment.\nCompensation at Pearson is influenced by factors including skill set, experience, and location.\nThe full-time salary range for this role is $140,000 - $160,000. This position is eligible to participate in an annual incentive program. Information on benefits can be found here.\nApplications will be accepted through 20th November 2026. 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