Consultant, User Experience: Cognitive Systems

About this role

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.

About the role 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. What you'll do 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 scope, what strong outputs look like, and how to improve funding and readiness conversations. • Translate complex human-AI and governance concepts into clear, practical guidance for both leaders and practitioners. • Represent human-AI teaming in cross-functional forums such as AI review and red-team discussions, endpoint-agent pilots, and platform control design.

What you bring Highly Preferred • 5+ years of relevant experience in cognitive systems engineering, human factors, human-systems integration, UX research/design, human-AI interaction, or a closely related discipline — with demonstrated senior/lead impact. • Expertise in designing human-machine or human-AI systems where human oversight, decision-making, and accountability are central. • Strong grounding in how modern AI systems (LLMs, AI agents) actually work, their behaviors, and their failure modes — enough to reason about risk and oversight rather than only use the tools. • Proven ability to create standards, frameworks, and rubrics that others adopt, and to raise the quality of work across a team or organization. • Experience translating governance, risk, or compliance requirements into practical controls and workflows. • Excellent facilitation, coaching, and written communication skills, including the ability to make complex concepts clear to non-technical leaders. • Demonstrated ability to mentor others and build shared ownership rather than becoming the single point of dependency.

Preferred • Background in cognitive systems engineering, cognitive science, human factors, decision science, or applied experimental psychology. • Experience with AI governance, model risk management, threat/red-team testing, or responsible-AI programs. • Familiarity with decision modeling and structured approaches to reasoning under uncertainty. • Experience in a regulated industry such as financial services or insurance. • Experience designing enablement, training, or upskilling programs at scale.

How you'll be measu

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