My process starts with problem framing, where I dive into user research data to validate assumptions and uncover real needs. By combining insights with data-driven analysis, I craft precise problem statements. To further refine these statements and draft product requirement documents, I leverage AI as my content design assistant.

With the product strategy in place, I craft high-fidelity designs in Figma, leveraging shared components and design libraries to ensure consistency. My focus is on delivering precise, pixel-perfect specs that enable seamless frontend development, while maintaining a design-first approach to quality and detail.

To test how the true end product experience feels, we validate our designs through interactive prototypes and user testing. This approach allows us to refine the experience, ensuring it's seamless and intuitive for our users, and making data-informed decisions to drive the design forward.

The heart of every great design system is a solid Style Guide. This establishes foundational elements like typography, color palette, grids, icons, and interaction settings. I've co-led visual language design sprints that define tone, mood, and aesthetics, setting a cohesive direction for marketing and product design teams to follow and build upon.

Component libraries empower cross-functional teams to build with shared components, driving efficiency and consistency. By leveraging these libraries, we reduce design and engineering debt, ensuring a unified experience across products and domains. Clear specs and guidelines enable frontend engineers to replicate dev libraries, aligning design and development efforts.

To ensure consistent application, we develop comprehensive guidelines that outline best practices for using our component libraries. These guidelines provide clear dos and don'ts, accompanied by visual examples, to help teams understand proper usage and avoid common pitfalls. By doing so, we empower new team members to quickly get up to speed and apply our design principles effectively.

We start with a research plan that outlines participant demographics, user tasks, and key insights. For qualitative feedback, in-person interviews provide rich, in-depth understanding of user thoughts and behaviors. Through these conversations, we uncover user needs, preferences, and pain points, gathering valuable insights that inform design optimizations and refine our approach.

For quantitative feedback, unmoderated tests provide valuable insights. We recruit participants that match our target demographics and design tasks that simulate real-world interactions. Participants share their thought process and rate their experience, giving us a deeper understanding of user behavior. We analyze the results, highlighting key quotes and clips that validate our findings.

To understand our target user base, we develop persona maps that outline key characteristics, needs, and pain points. These maps inform our design decisions, helping us create experiences that address user needs. Personas also guide our screener development, ensuring we recruit relevant participants for research.