Lead Specialist, AI Scientist

What matters most

• Deliver measurable learning outcomes

  Lead production AI for learner intelligence, recommendations, knowledge graphs and personalized learning. The posting names learner engagement, efficacy and business impact as expected results.

• Own AI from prototype through operation

  Take capabilities from experimentation to deployment, monitoring, evaluation and continuous improvement, rather than stopping at a working prototype.

• Build reusable production infrastructure

  Develop AI services, APIs and workflows for use across Pearson products, and establish MLOps and AIOps practices for observability, governance and reliability.

• Evaluate and govern generative AI

  Deliver LLM, RAG and agentic applications while evaluating foundation models for quality, safety, scalability, latency and cost and establishing responsible-AI practices.

• Lead across disciplines

  Partner with Product, Engineering, Design, Learning Science and Data Science; mentor technical colleagues; and communicate architecture decisions, risks and outcomes to stakeholders.

Likely challenges to think through

• Demonstrating learning efficacy

  A likely difficulty is distinguishing improved learner engagement from improved learning when assessing personalization or recommendations. Prepare to explain how you would define both measures with Learning Science and evaluate a release against them; the posting does not specify a measurement method.

• Balancing model performance and operating cost

  Foundation-model choices may trade answer quality and safety against latency, scale and cost. Prepare a comparison approach with evaluation criteria, operational thresholds and a rationale for when a simpler architecture would suffice.

• Reusing services without constraining products

  Shared AI platform components may speed adoption but need to accommodate different product workflows. Prepare to discuss which APIs, evaluation and monitoring practices you would standardize, and where you would leave room for product-specific design.

Topics to prepare

• RAG and agentic AI architecture

  The posting calls for LLM, retrieval-augmented generation (RAG), and agentic AI solutions. Sketch when a learning-product feature would use retrieval, an agentic workflow, or neither; explain the quality, latency, cost, and safety trade-offs. This is a suggested revision exercise, not a specified interview task.

• Recommendations and knowledge graphs for personalization

  Pearson names learner intelligence, recommendations, knowledge graphs, and personalized learning as target capabilities. On a hypothetical learning scenario, map how learner signals and content relationships could inform a recommendation, then identify what would need evaluation before deployment.

• Production AI lifecycle and platform design

  The role owns experimentation through deployment, monitoring, and improvement, and calls for reusable services plus MLOps and AIOps. Draw a conceptual architecture showing those stages and where observability, reliability, and governance belong.

• Model evaluation and responsible AI

  The posting explicitly calls for evaluating models and agents against quality, safety, scalability, latency, and cost. Draft a comparison rubric for two hypothetical foundation-model approaches, including failure cases and a decision rule; the rubric is a preparation suggestion, not a stated Pearson process.

• Educational measurement concepts

  Learning science, psychometrics, proficiency modeling, Bayesian methods, and item response theory appear under preferred qualifications. If relevant to your preparation, outline how proficiency estimates might differ from engagement metrics and what evidence would support a personalized-learning outcome; these methods are not listed as required.

Skills to revise

• Build a small Python AI API

  Python, APIs, testing, and foundation-model application development are explicit qualifications. As practice, implement a small API around a hypothetical learning-content task, add tests for normal and failure responses, and explain how you would version its interface.

• Prototype and assess a retrieval workflow

  RAG, prompt engineering, and vector databases are named in the posting. As a suggested exercise, prototype retrieval over sample content and compare responses with and without retrieval for grounding, incorrect claims, latency, and cost. No particular provider is required by this exercise.

• Create an evaluation and monitoring plan

  Pearson expects deployed AI systems to be evaluated, monitored, and improved. Prepare a compact test set and scorecard for a hypothetical recommendation or LLM feature, then specify what production signals would trigger investigation and how you would assess a change.

• Design a deployable service workflow

  The qualifications name cloud services such as AWS, containerization, orchestration, infrastructure as code, CI/CD, and production operations. Practice diagramming a containerized service's build, test, deployment, rollback, and observability path, explaining reliability and cost trade-offs without assuming Pearson's specific stack.

• Present an architecture decision

  The role requires collaboration across Product, Engineering, Design, Learning Science, and Data Science, as well as mentoring and stakeholder communication. Rehearse a brief decision memo for a hypothetical personalized-learning feature: state the outcome sought, alternatives, risks, evaluation plan, and what each partner would need to resolve.

• Select a production AI project walkthrough

  The role owns AI capabilities from prototype through deployment and improvement. Prepare one project you can substantiate, covering your decisions, production responsibilities, monitoring, and measured outcomes; distinguish your contribution from the team’s.

• Sketch a reusable learning-AI service

  The posting calls for reusable services and APIs supporting personalization, recommendations, or knowledge graphs. As a practice exercise, draw an architecture showing data flow, a model or retrieval component, API boundaries, and integration points; explain the trade-offs.

• Prepare a foundation-model evaluation plan

  Pearson emphasizes quality, safety, scalability, latency, and cost. Draft a comparison of plausible model or RAG approaches, with evaluation criteria, test cases, failure modes, and a decision rule; label proposed metrics as your suggestions.

• Gather production engineering evidence

  The qualifications name Python, APIs, testing, CI/CD, cloud deployment, containers, orchestration, infrastructure as code, and MLOps. Assemble specific examples you can verify, showing what you built or operated and how reliability and observability were handled.

• Connect technical choices to learner outcomes

  Expected results include learner engagement and efficacy as well as business impact. Prepare a concise explanation of how you would evaluate a personalization or recommendation capability; treat learning-science and educational-measurement methods as preferred, not required.

• Rehearse a leadership decision story

  The role involves cross-functional work, mentoring, and communicating risks and trade-offs. Prepare one verifiable example that shows stakeholder input, your technical recommendation, how you supported others, and the outcome.

• Write targeted questions for Pearson

  Ask which AI capabilities are priorities, how learner outcomes and production quality are measured, and what platform components already exist. Clarify the posting’s conflicting location information: Austin, TX and Hybrid in the header versus Remote, United States in the role description.