Product Manager II, Analytics

RELX•Philadelphia, PA

20h

About The Position

We’re seeking a Product Manager II to improve analytics experiences for customers and internal teams across our Healthcare Education portfolio. You’ll own defined product initiatives from discovery through launch, working with cross-functional partners to turn user needs and data into practical solutions. You’ll also contribute to our direction for AI-enabled analytics and help ensure these capabilities are useful, trustworthy, and responsible.

Requirements

3+ years of experience in product management, technology, analytics, data products, or a related field.
Experience contributing to digital product delivery, from defining requirements through launch and iteration.
Familiarity with analytics, data products, or AI-enabled products, and how they can address user needs.
Ability to translate complex data or technical concepts into clear product requirements and customer value.
Strong communication, collaboration, and problem-solving skills.
Ability to manage priorities and dependencies in an agile environment.

Nice To Haves

Experience in healthcare, education, healthcare education, or enterprise software a plus.

Responsibilities

Own analytics features and initiatives from discovery and prioritization through delivery, launch, and iteration.
Clarify user problems and business goals through customer research, feedback, product data, and input from customer-facing teams.
Prioritize opportunities based on customer value, business impact, effort, and dependencies.
Translate needs into clear product requirements, user stories, and acceptance criteria.
Partner with Product, Engineering, Data Science, UX, subject matter experts, and customer-facing teams to develop and launch solutions.
Identify ways analytics and AI can simplify workflows, surface insights, and support decision-making.
Maintain a prioritized backlog and communicate progress, decisions, dependencies, and tradeoffs.
Track adoption, usability, and customer or business impact; use results to guide improvements.
Consider data quality, privacy, transparency, and user trust when working with AI-enabled products.