My Approach
How I Approach Complex Product Problems
Ambiguity is a natural part of product and business strategy development, and I approach it by first creating clarity around the problem space. I start by understanding the product vision, the business objectives behind the work, and the constraints shaping the project — such as timeline, technical limitations, or organizational priorities. I then communicate these factors openly with stakeholders so we have a shared understanding of what questions research should answer.
Once that foundation is established, I design research that fits the context of the project. Rather than defaulting to a single method, I choose approaches that best serve the goals and constraints of the work. After conducting the studies, I focus on synthesizing findings into clear insights that support product decisions. The goal is to move from ambiguity to informed direction while keeping enough flexibility for discovery along the way.
My Research Philosophy
Context Shapes Insight
My background in English Literature trained me to analyze meaning through context, which I bring into UX research. By understanding the broader ecosystem around users, including their motivations, behaviors, and environments, I help product teams uncover insights that inform product strategy, not just usability fixes.
Align User Value with Business Strategy
Impactful research sits at the intersection of user needs and business goals. I translate user insights into strategic recommendations that inform product roadmaps, reveal new market opportunities, and support long-term product growth.
Lead Through Insight and Collaboration
I see research as a catalyst for better product decisions. By working closely with designers, product managers, and engineers, I help teams ask the right questions, build shared understanding, and act confidently on research insights.
My use of AI as a Research Copilot
I integrate AI tools into my research workflow as a copilot that supports (but does not replace) human judgment. AI can help speed up time-consuming tasks such as summarizing transcripts, identifying emerging patterns across large qualitative datasets, and writing Python code for quantitative survey data analysis. This allows research projects to move faster while still maintaining depth and rigor in analysis.
However, I think insight generation remains a human-led process. AI outputs often surface interesting directions, but meaningful insights come from interpreting those signals within the broader context of user behavior, business goals, and product constraints. By keeping a strong human-in-the-loop approach, I use AI to support the research process while ensuring the final insights remain thoughtful and grounded for product teams and business stakeholders.