UX RESEARCH / COMPLEX PLATFORMS
I’m Mathias Burton, a Seattle-based senior researcher and product leader. My practice centers constructivist grounded theory, strengthened by data science and triangulation. I work where the users are expert, the systems are complicated, and the next product decision is not obvious.
TRACE THE EVIDENCE ↓The deliverable is not merely a report or a verdict. It is a defensible model of the opportunity—rich enough to generate ideas and precise enough to guide product, design, and engineering decisions.
My practice is rooted in constructivist grounded theory: data collection and analysis evolve together, interpretations stay accountable to context, and constant comparison turns observations into a model a team can challenge and use.
RefWorks / longitudinal research program ↗I learn the domain, architecture, data flows, constraints, failure modes, and working rhythms around an interface—then stay embedded with product and engineering as the model becomes software.
Socrata / open-data ingress ↗Interviews and contextual inquiry reveal the model. Product analytics, coded NPS, surveys, market data, and technical evidence test its reach. Prototypes expose the next uncertainty. Agreement—and productive contradiction—sharpens the decision.
PitchBook / evidence → roadmap ↗Research is a foundation for invention, not a gate at the end. I turn evidence into rich opportunity spaces, system models, working principles, and prototypes that give designers and engineers valuable ideas to build from.
AI experiments / research through making ↗Each story makes the same chain visible: the question, the evidence, the decision, the collaboration, and what changed.
Contextual inquiry, role and workflow modeling, and quantitative transaction analysis distinguished technical user segments and their decisions. The resulting model shaped requirements, integration work, and a multi-year product roadmap.
Across a technical civic-data portfolio, I studied how government teams sourced, ingested, validated, corrected, modeled, published, and used data. I triangulated contextual research and advisory programs with analytics, NPS, usability evidence, system models, and engineering constraints—then worked directly with the ingress team to turn that evidence into releasable software.
Field visits, researcher interviews, observation, diary work, surveys, coded NPS, competitive analysis, usability testing, and co-creation moved RefWorks from a citation manager toward a reusable research environment.
I use models, coding agents, and live websites as research-through-design instruments. The work grew into 30 project volumes and 150 experiment outlines that pair a polished, convincing artifact with the imperfections it reveals: the tension, evidence, and next version.
The change was not moving away from polish. It was learning not to confuse polish with completion.
Making something convincing exposes where the interaction breaks, the premise weakens, the model overreaches, or the next question appears. Those imperfections become evidence for the next iteration.
A polished prototype could be mistaken for the conclusion.
Polish makes the idea concrete enough to judge; its imperfections become the agenda for the next iteration.
Each conversation could disappear into its own project history.
A five-part format now preserves premise, build, tension, evidence, and next move across an accumulating archive.
The visible output received most of the attention.
The polished output stays in the record alongside the hidden labor, confident mistakes, rejected decisions, and changed opinions that made it useful.
Experiments were individual acts of making.
They became a production and learning system: daily actions, related derivatives, weekly evidence beats, review, and explicit proof levels.
EVIDENCE STANDARD / The archive distinguishes live artifacts, working prototypes, adjacent studies, and concept studies. Planned reach and publishing targets remain targets until verified.
THE THROUGH-LINE
I’m at my best with autonomous, technically demanding research: learning the system, finding the consequential question, conducting the work, and staying with the finding until it becomes a decision.
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