Lead Specialist, AI Scientist

What matters most

• Turn AI prototypes into production outcomes

  The role owns AI capabilities from experimentation through deployment, monitoring, evaluation and improvement. The posting emphasizes measurable learner and business outcomes, including engagement and learning efficacy.

• Deliver personalized learning capabilities

  Expected results include production-ready learner intelligence, recommendations and knowledge graphs that support personalized learning experiences, alongside LLM, RAG and agentic AI applications.

• Build reusable, reliable AI services

  The role calls for shared platform components, APIs and workflows, plus MLOps and AIOps practices for observability, governance and reliability. Expected systems are secure, observable and cost-efficient in production.

• Set technical direction across teams

  The specialist partners with Product, Engineering, Design, Learning Science and Data Science; evaluates architectural trade-offs; mentors technical colleagues; and communicates risks, decisions and results to stakeholders.

Likely challenges to think through

• Proving learning value beyond model quality

  A likely challenge is connecting model performance to learner efficacy and product impact, rather than treating a successful prototype as the outcome. Prepare an example of how to define both technical evaluation measures and product or learning measures, then use results to guide iteration.

• Choosing an architecture under competing constraints

  The posting explicitly calls for balancing quality, safety, scalability, latency and cost when evaluating models and architectures. Prepare to explain how you would compare approaches for a learning use case, document trade-offs and decide what needs production monitoring.

• Balancing shared infrastructure with product needs

  Because the role spans reusable platform services and multiple Pearson products, a plausible difficulty is deciding what to standardize without slowing a specific delivery. Prepare an example of separating common APIs, evaluation and operational controls from use-case-specific logic, and explaining that boundary to cross-functional partners.

Topics to prepare

• Production AI lifecycle

  The role owns AI capabilities from experimentation through deployment, monitoring, evaluation and improvement. Suggested revision exercise: explain the decisions and evidence needed at each stage to move a learner-intelligence prototype into production.

• RAG and agent architecture

  The posting calls for LLM, retrieval-augmented generation and agentic AI solutions. Suggested revision exercise: compare a retrieval-based design with an agentic workflow for a learning-product use case, including where each could fail.

• Recommendations and knowledge graphs

  Production-ready recommendation and knowledge graph capabilities are expected results. Suggested revision exercise: sketch how learner signals and a knowledge graph could inform personalized recommendations, and identify what would need evaluation.

• Model selection and responsible AI

  Pearson asks this role to balance quality, safety, scalability, latency and cost, and establish evaluation and governance practices. Suggested revision exercise: create a decision matrix comparing two foundation-model approaches against those criteria.

• Learning measurement context

  Learning science and concepts such as proficiency modeling and item response theory are preferred, not required. Suggested revision exercise: review how a proficiency estimate differs from a recommendation signal and what evidence would support a learning-efficacy claim.

Skills to revise

• Build and test a Python AI API

  Python, APIs, testing, CI/CD and version control are stated qualifications. Suggested exercise: implement a small foundation-model-backed API, add tests for failures and output handling, and outline its CI/CD checks.

• Prototype a retrieval workflow

  Hands-on foundation-model development and RAG are named in the posting. Suggested exercise: build a small retrieval-backed response workflow using a technology named in the posting, then inspect responses for unsupported claims and retrieval failures.

• Evaluate an AI workflow

  The role evaluates models and agents for quality, safety, reliability, latency and cost. Suggested exercise: define a small test set, run two model or prompt variants, and summarize the measured trade-offs and failure cases.

• Plan production deployment and observability

  Cloud deployment, containerization, orchestration, infrastructure as code, MLOps and AIOps are explicit requirements. Suggested exercise: diagram a deployable AI service on AWS or a similar cloud platform, including monitoring, rollback and cost visibility.

• Communicate architecture trade-offs

  The role partners with Product, Engineering, Design, Learning Science and Data Science and communicates decisions to stakeholders. Suggested exercise: prepare a brief architecture review that states the learner outcome, alternatives, risks, evaluation plan and decision in terms each partner can use.

• Build a production AI project walkthrough

  The role owns AI capabilities from prototype through monitoring and improvement. Prepare a concise walkthrough of a relevant project, if available, covering your decisions, deployment, operational lessons, and measured outcomes; identify any stages you did not own.

• Sketch a personalized-learning system

  As interview practice, draw a hypothetical architecture for learner intelligence, recommendations, or a knowledge graph. Explain data flow, reusable APIs, cloud deployment, and how you would test whether it improves learner outcomes.

• Prepare evidence of production engineering

  The qualifications name Python, APIs, testing, CI/CD, cloud-native deployment, containers, orchestration, infrastructure as code, and MLOps. Assemble specific examples or artifacts for the areas you can substantiate, including reliability and observability decisions.

• Develop an AI evaluation plan

  For a hypothetical foundation-model or RAG feature, outline how you would compare approaches on quality, safety, latency, scalability, and cost. Include monitoring, failure handling, and responsible-AI checks; do not present the exercise as Pearson’s existing process.

• Rehearse a cross-functional leadership example

  Prepare one account, if available, of working with product and technical partners, explaining trade-offs to stakeholders, or mentoring colleagues. State your contribution and the result without implying experience you cannot document.

• Write focused questions for Pearson

  Ask which learner intelligence, recommendation, or knowledge graph outcomes are the near-term priority and how success is measured. Clarify the posting’s conflicting location labels: the role description says remote in the United States, while the page lists Boston and hybrid.