Available for select advisory & senior/staff research roles
I’m a strategic, mixed-methods UX researcher who helps product teams navigate ambiguity and make confident decisions.
I specialize in uncovering the “why” behind user behavior and translating insight into clear product direction, scalable systems, and measurable impact. My work sits at the intersection of user psychology, business strategy, and design execution.
I don’t just validate features; I identify risk, clarify mental models, and ensure that research informs roadmap decisions early enough to matter. Whether demystifying product education, refining discovery flows, or shaping launch strategy, I approach research as a lever for reducing uncertainty and strengthening product outcomes.
“I thrive in environments where clarity isn’t handed to you: where the real work is defining the problem before solving it.”
It all begins with a problem statement. The crux of an idea is determining if and what the challenge is that requires a design solution.
Principle // 01
Active Engagement · Full-TimeHuman Factors & UX
Human Factors Engineer & UX Researcher
Government Entity · Enterprise Infrastructure
Currently working to modernize country-wide user flows and architect resilient software interfaces for high-assurance public operators. My research informs mission-critical decision support, workflow streamlining, and error-prevention frameworks.
Standardized milestones from root ambiguity to measurable software impact, featuring concrete operational artifacts and deliverable syntheses.
Enterprise / Gov PlatformHigh-Assurance Systems
Country-Wide Workflow Modernization
Streamlining high-friction operator handoffs and nationwide intake procedures for federal personnel across legacy distributed environments.
Quantified Impact
−38% Task Time
99.4% Error-Free Intake
01 · ProblemLegacy Operator Bottlenecks
02 · MethodsContextual Inquiries & CTA
03 · ExecutionBenchmarking & Formative Labs
04 · InsightsDual Mental Model Mismatch
05 · ImpactNationwide Rollout & −38% Time
crisis_alert Problem & System Tension
Operators managed 14 disjointed legacy screens during emergency intake handoffs, causing severe cognitive friction, redundant manual data reconciliation, and recurring procedural delays.
“I spend more mental effort double-checking that the terminal didn't lose state during the handoff than I do evaluating the citizen's actual case priority.”
format_quote Voice of Customer · Field Intake Specialist (Region IV)
insights Executive Finding & Mental Model Realignment
By replacing disjointed database record lookups with unified situational status cards, cognitive load shifted from manual memory recall to visual recognition. Operators moved from a multi-window mental model to an event-driven queue.
Research Artifacts & Deliverables
hub
Operator Journey Map & Friction Heatmap48-step end-to-end procedural audit across 4 field offices
sticky_note_2
Affinity Diagramming Synthesis320+ field observations categorized into 6 core failure modes
schema
Information Architecture Tree TestingQuantitative validation with n=64 enterprise operators
inventory_2 Deliverable: IA Spec + Design Tokens
AI Systems · HCIAlgorithmic Transparency
Decision-Support AI Interface
Investigating algorithmic trust, confirmation bias, and cognitive overload in automated recommendations through rigorous cognitive walkthroughs.
Quantified Impact
+92% Trust Score
−64% Cognitive Overload
01 · ProblemBlack-Box Skepticism
02 · MethodsCognitive Walkthrough Protocols
03 · ExecutionWizard-of-Oz Simulation Labs
04 · InsightsConfidence Intervals Over Precision
05 · ImpactHuman-in-the-Loop Standard
warning Problem: Automation Bias & Rejection
Decision-makers actively bypassed automated triage recommendations because output ratings lacked explainability, resulting in either uncritical rubber-stamping or complete operational abandonment.
“A 98% match number means nothing to me under pressure unless I can see which underlying feature weighting triggered the high confidence flag.”
format_quote Voice of Customer · Senior Systems Analyst
psychology Executive Finding: The Explainability Matrix
By replacing static probability percentages with an interactive rationale matrix (showing contributing features vs. contradictory indicators), decision times decreased while operator agency and verification accuracy jumped to 94%.
Cognitive Walkthrough Matrix24 scenario runs scoring user intent vs system response
stacked_line_chart
NASA-TLX Cognitive Workload StudyStandardized psychometric battery before & after UI redesign
inventory_2 Deliverable: Explainable AI Guidelines
Growth · ActivationBehavioral Telemetry
Product Education & Discovery Optimization
Demystifying onboarding drop-off by correlating in-depth qualitative discovery with telemetry funnel logs to drive autonomous product adoption.
Quantified Impact
+34% Activation
−52% Support Tickets
01 · ProblemDay-7 Funnel Churn
02 · MethodsMixed Qual-Quant Cohorts
03 · Execution1:1 Discovery & Funnel Log Analysis
04 · InsightsPremature Configuration Fatigue
05 · ImpactAdaptive Progressive Disclosure
crisis_alert Problem: Steep Learning Curve in Critical Path
Telemetry revealed 62% of new enterprise admins dropped off at Step 3 of onboarding due to required upfront server permissions before perceiving product value.
“I wanted to test the reporting engine first, but the setup wizard forced me to invite my entire team and grant OAuth scopes before I even saw a sample chart.”
format_quote Voice of Customer · Platform Lead
visibility Executive Finding: The “Sandbox-First” Mental Model
By replacing mandatory upfront setup with an interactive sandbox populated with pre-computed dummy telemetry, users achieved the “aha moment” in under 3 minutes, unlocking autonomous permission grant adoption.
Research Artifacts & Deliverables
bar_chart
Funnel Telemetry Drop-off MapSynthesis of n=4,200 onboarding sessions with event logging
groups
Archetype Persona & Needs MatrixEvaluated Evaluator vs Operator mental models
flaky
Formative Usability Test ProtocolIterative testing of progressive disclosure patterns (3 rounds)
Grounding design systems in human capability, technical feasibility, and continuous empirical validation.
[ Phase 01 · Milestone ]
Discover
Ensuring the problem is well defined is critical to the process. Discovering and defining the problem often starts with talking with stakeholders and people using the product.
Building encompasses sketches, prototypes, more research, and engaging with developers. Knowing what is possible within time and resource constraints informs research goals.
Continual improvement is the goal and that requires iteration. That may mean new features or smaller changes that will streamline workflow and improve time-on-task metrics.
Tangible Artifacts Produced
speed System Usability Scale (SUS) Scorecards
timer Time-on-Task Telemetry & Funnel Logs
autorenew Longitudinal Feedback Tracking Loops
Qualifications & Perspective
The Marriage of Technology & Humanness
I’ve been an IT professional for many years and love this iteration as a UX researcher. I enjoy the marriage of technology and humanness; it puts my skills to their best use.
I would say I am a qualitative researcher with a healthy dose of quant. I enjoy delving into the numbers especially if it leads to more research opportunities! Combining deep technical systems architecture knowledge with rigorous cognitive inquiry allows me to bridge the translation gap between product managers, engineers, and users.
terminal
HeritageDeep Systems IT Roots
analytics
EquilibriumQual Heart + Quant Discipline
Start a Conversation
Get in Touch
Available for staff-level UX research roles, human factors advisory consulting, and specialized enterprise systems evaluation.
person Germaine Irwin
location_on Aurora, Colorado · Mountain Time (MT) · Open to Hybrid/Remote
badge HFES · Senior Researcher
Direct Verification
Let’s Build Clarity Together
Need to de-risk a high-stakes roadmap or evaluate algorithmic system interfaces? I welcome conversations with executive design leaders and recruiters.