Jobs at The Learning House

Leads Wiley’s Enterprise Data Platform product domain, owning its roadmap, prioritization, adoption, team development, and business outcomes. Partners with senior business and technology stakeholders to simplify the data landscape, improve governance and reuse, and deliver scalable capabilities for trusted decision-making. The role requires strong enterprise product leadership, data platform expertise, stakeholder influence, technical understanding, and experience developing teams and product management practices.

Drive B2B demand generation for Wiley’s AI, Analytics and Data solutions. Own audience targeting, campaign planning, execution, optimization and reporting across email, paid social, content syndication, webinars and events. Partner with Sales to define lead criteria, improve lead quality and generate pipeline. Use funnel and campaign data to optimize targeting, messaging, costs and return on investment while adapting commercial content for different audiences.

Define and document business and system requirements for editorial/manuscript workflows, support product roadmap prioritization, assist implementation and UAT, act as contact during rollout, and drive adoption across cross-functional teams.

Own production NLP and applied AI systems that enrich scientific literature with entities, classifications, claims, and summaries. Design and evaluate classical NLP, embedding, retrieval, and LLM approaches; build scalable Python pipelines; manage cost, concurrency, reliability, and model quality. Collaborate with data engineers on Airflow and Dagster orchestration, contribute to agentic AI applications, and work with editors, product managers, and engineers to deliver research intelligence to users.

Build and operate NLP enrichment pipelines over scientific literature, extracting entities, classifications, claims, and summaries. Compare classical NLP, retrieval, embedding, LLM, and fine-tuned model approaches using evaluations, cost, quality, and operational metrics. Create golden datasets, define evaluation strategies, contribute to agentic AI applications, and collaborate with editors, product managers, engineers, SMEs, and vendors to deliver production modeling systems.