Lead Specialist, Product Management

Pearson•United States,

1d•$100,000 - $145,000•Remote

About The Position

Pearson is seeking a Product Manager to help shape the next generation of AI-powered learning experiences. This role sits at the intersection of product strategy, learning design, data, AI technology, and user experience. You will lead product discovery, definition, delivery, launch, and optimization for AI-enabled features that help students learn more effectively. This may include AI tutoring, personalized practice, study recommendations, agentic learning workflows, content generation, feedback systems, and tools that help students understand what to study next. This is a hands-on product role. You will not only define the roadmap; you will work directly with engineering, design, data science, learning science, content, QA, legal, privacy, and business stakeholders to turn ambiguous opportunities into trusted, measurable product experiences. A strong candidate will be comfortable using AI in their own product workflow, while also understanding how to build AI into products safely and responsibly.

Requirements

3–5+ years of product management experience for digital, software, SaaS, consumer, education, or platform products.
Demonstrated experience shipping products or features from discovery through launch and post-launch optimization.
Hands-on experience using generative AI tools in product work, such as research synthesis, prototyping, analysis, requirements, experimentation, or workflow automation.
Practical understanding of LLM-based products, including prompts, model behavior, retrieval-augmented generation, evaluation, hallucination risk, and human-in-the-loop workflows.
Strong product judgment with the ability to balance user value, business value, feasibility, usability, safety, and operational constraints.
Strong analytical skills and experience using product data, experiments, user research, and qualitative feedback to make decisions.
Clear written and verbal communication skills, with the ability to translate between technical and non-technical audiences.
Ability to work effectively with engineering, design, data, QA, content, learning science, legal, privacy, and business stakeholders.

Nice To Haves

Experience building or managing AI-powered, ML-powered, adaptive, personalized, or data-driven products.
Experience in EdTech, assessment, tutoring, learning platforms, content platforms, or other trust-sensitive domains.
Familiarity with AI evaluation methods, model monitoring, prompt testing, red teaming, test-set creation, or rubric-based review.
Familiarity with agentic workflows, tool use, vector databases, embeddings, RAG systems, LLM orchestration, or model cost/latency trade-offs.
Experience with A/B testing, funnel analysis, retention analysis, learning outcome measurement, or product analytics platforms.
Comfort creating lightweight prototypes using AI-assisted tools, no-code tools, notebooks, or design prototypes to reduce product ambiguity.
Working knowledge of agile product development practices.

Responsibilities

Support the roadmap and success metrics for AI-powered learning experiences within Pearson’s digital learning ecosystem.
Identify opportunities where AI can improve student outcomes, engagement, personalization, and product quality through user research, data analysis, and experimentation.
Contribute to the end-to-end delivery of AI-enabled features, from discovery and definition through launch and ongoing optimization.
Translate learner, business, and technical needs into clear product requirements, user stories, and prioritized work.
Partner with engineering, design, data science, learning science, content, QA, and AI platform teams to deliver effective AI-powered experiences.
Use AI tools to accelerate product discovery, research synthesis, prototyping, documentation, and stakeholder communication.
Help define and evaluate AI-powered learning experiences, including prompt design, workflow behavior, quality standards, and student trust considerations.
Monitor product performance using analytics, user feedback, and experimentation results to drive continuous improvement.
Ensure AI features align with Pearson standards for privacy, security, accessibility, academic integrity, and responsible AI.