Job Description

Pearson plc

As the world’s largest FTSE 100 learning company, Pearson has been contributing to a profound impact on our world since its formation in 1844. Pearson employs more than 20,000, operates in 70 countries, and delivers products and services in nearly 200 countries. We work toward a common purpose - to help everyone achieve their potential through learning. We do that by developing and providing high-quality, digital content and learning experiences, as well as assessments and qualifications, that help people build their skills and grow with the world around them. Unveil the boundless possibilities that await you.

Principal AI Scientist, Skills & Assessment in the Flow of Work

Location: Poland, Spain and UK

AI Scientist, Technology, Data Engineering

About the Role

The Principal AI Scientist, Skills & Assessment in the Flow of Work, is a senior scientific leadership role responsible for the design, validation, and continuous improvement of AI -driven learning and assessment products embedded directly into the flow of work.

This role combines deep expertise in psychometrics and measurement, applied AI and machine learning, and science led product ownership to deliver reliable, fair, explainable, and scalable -next generation assessment and skills solutions. Operating with high autonomy, the Principal AI Scientist translates applied research into production- ready systems, establishes- scientific and methodological standards, and acts as a steward of scientific quality and risk at scale.

The role partners closely with product leaders, AI engineers, subject matter experts, and internal and external stakeholders—including large enterprise clients—to ensure scientific rigor, platform scalability, and measurable learner and business impact.

Key Responsibilities

• Science Product Ownership & Assessment Leadership

• Serve as a Science Product Owner for a portfolio of AI driven- assessment, learning, and skills products embedded in the flow of work.

• Define and own the scientific and methodological roadmap for AI enabled- learning and assessment solutions, balancing innovation, rigor, scalability, and business outcomes.

• Act as the final scientific authority on assessment and measurement decisions within scope, including trade-offs related to reliability, validity, fairness, explainability, and operational constraints.

• Coordinate and technically lead a distributed, multidisciplinary group of subject matter experts contributing to algorithm and workflow design.

• Drive the translation of applied research (e.g., skills inference, ambient assessment, adaptive measurement, automated content or item generation) into robust, production ready- AI systems.

• Establish reusable scientific design patterns, evaluation frameworks, and lifecycle practices for AI enabled- assessment at scale.

• Design, execute, and oversee validation studies to ensure assessments meet accepted standards of reliability, validity, fairness, and ongoing performance stability.

• Define, monitor, and interpret scientific KPIs in production, including drift detection, bias indicators, and outcome consistency.

• Take explicit accountability for scientific and measurement risk, identifying and mitigating risks associated with AI -based assessment at scale.

Applied AI & Modern AI Systems

• Apply modern AI and ML techniques—including large language models and related foundation models—where appropriate to support learning and assessment use cases.

• Contribute to the design and evaluation of LLM enabled- components (e.g., content generation, skills inference, feedback, or reasoning support), ensuring they are scientifically defensible and fit for purpose.

• Support the use of AI driven and agentic workflows in learning and assessment contexts, with clear scientific guardrails around validity, bias, robustness, and appropriate use-.

• Partner closely with engineering teams to ensure scientific intent is preserved in production implementations, including evaluation, monitoring, and lifecycle management.

• Ensure alignment between scientific methodology and system behavior as AI systems evolve over time.

Thought Leadership & Strategic Influence

• Generate research based- thought leadership using large-scale learning and assessment data to support product strategy, marketing, sales, and client engagement.

• Represent Pearson’s scientific credibility through whitepapers, client briefings, and internal strategy discussions.

• Influence product, platform, and AI strategy through evidence based- insight and scientific authority, rather than formal authority.

• Contribute to internal and external conversations on responsible AI, particularly in the context of learning and assessment.

Qualifications

• Advanced degree (MS or PhD preferred) in Psychometrics, Educational Measurement, Statistics, Machine Learning, Data Science, AI, or equivalent depth of professional experience.

• Demonstrated experience leading scientifically rigorous, AI enabled- assessment or measurement systems from research through production.

• Deep expertise in measurement theory, validation methodologies, and applied statistical modeling in real-world- contexts.

• Strong applied experience with AI and machine learning systems, including collaboration with engineering teams on scalable implementations.

• Working knowledge of modern AI systems, including large language models, sufficient to evaluate their use in learning and assessment contexts.

• Proven ability to lead through scientific expertise and credibility, influencing product and platform direction without direct people management.

• Experience operating effectively in high-ambiguity, high-impact problem spaces with material business, ethical, or reputational risk.

Strong Plus

• Experience applying LLMs or similar models in learning, assessment, or skills related- products.

• Experience with continuous or ambient assessment, adaptive testing, or automated content generation.

• Experience contributing to AI agentic workflows evaluation, monitoring, or governance practices.

• Background in learning science, education technology, or workforce skills ecosystems.

• Experience working with enterprise scale platforms and long--lived production- AI systems.