• Lead the Product Discovery & Scoping Process • Define the product vision and roadmap in partnership with relevant aiEDU leaders and stakeholders • Define the problem space, surface constraints, and document current-state pain points • Conduct stakeholder interviews, user testing and other research to guide product development and feature prioritization • Translate requirements into a phased roadmap with clear milestones • Partner with our VP of Operations to identify and address relevant security, safety, privacy and legal considerations. • Develop and maintain a product requirements document (PRD) or equivalent • Build the product end-to-end across a 2-3 month window, coordinating across aiEDU stakeholders to maintain alignment. Ideally, you can get the product to 90% yourself, and then partner with a contracted engineer to ensure the product is ready for full deployment. • Define evaluation criteria and product analytics aligned with aiEDU's mission and operational needs; evaluate and refine product as needed. • Coach and upskill aiEDU’s own staff throughout the engagement, sharing best practices for product development and creating documentation, templates and frameworks that can be used for future product development. • Support our Operations team in advancing the aiEDU’s team internal AI readiness, advising as relevant on AI-assisted product development, AI augmented workflows and the shared infrastructure required to take our work to the next level.

• Define and drive product strategy • Lead through ambiguity at scale • Deliver high-impact outcomes • Elevate the product organization • Build deep domain expertise

• Oversee all downstream uses of graph products, both internal and external, to ensure maximum performance against all use cases • Convert needs into requirements for the ongoing build and maintenance of our online and offline identity graphs • Work with product owners and technical development teams to guide the delivery of product roadmaps for new graph products and enhancements • Monitor performance metrics of our graph products and lead opportunities to improve performance metrics • Oversee privacy and data governance requirements for all graph products and derivatives • Engage with data partners to improve data supply networks and ensure upstream data quality • Guide improvement projects by partnering with the Experian strategy, data science and development teams to test new MVPs that solve market problems • Implement the defined future state plans and strategies for building our graphs • Participate in trade association working groups within the data-driven marketing ecosystem (ie: NAI, IAB)

• Drive the end-to-end H3 discovery process—framing the jobs-to-be-done (JTBD), building prototypes, and managing low-fidelity experiments. • Collaborate with your Engineering counterpart to ship weekly "sketchy V0s" and fake-door experiments rather than scalable V1s, optimizing for speed of validated learning. • Talk to target users, alpha participants, and industry edge-cases constantly. Build deep qualitative empathy to uncover latent human needs from real workflows, not feature requests. • Obsess over a strict succession of metrics before considering delivery economics or monetization. • Actively evaluate data points from weekly probes to confidently make "Persevere, Pivot, or Kill" recommendations—treating a quick, low-cost "Kill" as a major win. • Maintain a disciplined focus on radical innovation, calling out and rejecting ideas that are merely incremental optimizations.