Consultant, User Experience: Cognitive Systems

If you’re passionate about becoming a Nationwide associate and believe you have the potential to be something great, let’s talk. At Nationwide®, “on your side” goes beyond just words. Our customers are at the center of everything we do and we’re looking for associates who are passionate about delivering extraordinary care.

Nationwide is building a durable, enterprise practice for how people and AI work together responsibly. As AI moves from assisting to acting — from answering questions to taking multi-step action across our tools, endpoints, and business processes — we need someone who can design the human-AI systems that keep judgment, oversight, and accountability firmly in human hands. The Cognitive Systems Engineer sets the standards for Human-AI Teaming (HAT) and the governance controls that make AI adoption safe, effective, and scalable. This is an experienced individual-contributor consultative role. Alongside setting standards, you will remain hands-on — doing project work directly, applying HAT and governance methods on live initiatives so the standards stay grounded in real practice. You will define how we design, test, and evaluate human-AI systems; mentor practitioners and people leaders; and partner across UX, Technology, AI Risk, and platform teams. You will be positioned to co-own — and over time own — a core enterprise capability, reducing the organization's dependence on any single expert and making responsible AI a repeatable discipline rather than a heroic effort.

Set the standard for Human-AI Teaming Establish shared quality criteria for HAT work — evaluating systems against the qualities of being aligned, understandable, controllable, and resilient. Design coordination patterns that make explicit how authority, proactivity, input, and output are divided between humans and AI, and how the level of AI authority and initiative changes the way the team must communicate and stay coordinated. Frame problems around the decision to be made and the human oversight required, rather than the tool or model. Embed governance and controls Translate enterprise AI governance expectations into practical, usable controls, guardrails, and review practices that teams can apply in planning, development, and production. Define control objectives for meaningful human judgment, safe handoffs, failure modes and fallback paths, attribution, and kill-switch / intervention decision rights for increasingly autonomous agents. Extend a platform-agnostic control set across agentic platforms and endpoints, clarifying what platform owners, risk partners, and product teams each own. Partner with AI Risk, Information Risk Governance, model risk management, and security on threat testing and pre-release readiness for AI agents — including agents built by citizen developers. Build capability in others (reduce single-threaded dependency) Mentor and coach UX practitioners, engineers, and people leaders, moving them from awareness into advanced, routine HAT execution: problem scoping, prompt shaping with decision context, coordination-pattern design, and review against HAT principles. Create reusable frameworks, job aids, and enablement so responsible AI practices are embedded in everyday work rather than dependent on individual experts. Do the work on live projects Apply HAT and governance methods directly on active projects — scoping problems, shaping decision-context prompts, designing and reviewing coordination patterns, and evaluating systems against the four qualities. Partner hands-on with practitioners and citizen developers on real AI and AI-agent work, including heuristic evaluation and threat-testing readiness, rather than advising only from the sidelines. Use project work as a proving ground for the standards, surfacing gaps and improving the frameworks based on what real delivery demands. Advise and influence Serve as a trusted advisor to leadership on where HAT should be in