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My day-to-day work involves designing and launching platforms for Fortune 500 and global life sciences companies. These projects sit at the intersection of design, data, and delivery — translating ambiguous stakeholder needs into structured workflows that help organizations make more defensible, data-driven decisions. Due to client confidentiality, I can’t share deliverables, but here’s how I approach these problems.
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A global, multi-business-unit organization ran its annual Enterprise Risk Assessment through manual follow-ups, inconsistent scoring definitions, and version-controlled documents. Leadership, Internal Audit, and functional contributors were all feeling the friction — inconsistent evaluation, low visibility, high coordination burden, and no traceability from risk identification to remediation action.
End-to-end design lead. I drove process design, workflow and screen design, and stakeholder alignment across Internal Audit, senior leadership, and functional contributors. I partnered with engineering to translate workflow intent into feasible system logic within an existing compliance platform.
I’d formalize a reusable definition design system for rating scales and tooltips — this project showed how much senior stakeholder trust depends on precise wording. I’d also build more explicit “next best action” landing experiences per user role to reduce cognitive load.
Conflict of interest reviews are a common pain point across regulated industries — manual, inconsistent, and difficult to scale. My company saw an opportunity to build a reusable AI agent that could standardize this process and offer it as a solution to clients. The challenge was designing something flexible enough to work across different organizations and policy frameworks while being rigorous enough to hold up in regulated environments.