About Me
Building knowledge systems where people and AI work together.
I'm an applied data scientist specializing in knowledge systems — NLP, classification, retrieval, and RAG pipelines built around real knowledge workflows. I work at the intersection of people, process, knowledge, and AI, with a focus on building the right foundations before layering in the technology.
I'm actively involved in KCS adoption within my organization, including training, coaching, and building the data and AI tools that support the practice. My long-term direction is toward knowledge systems design, human-centered AI, and organizational learning.
My Story
I started my career on the frontline of customer support, and I noticed early on that the metrics we were measured on had almost nothing to do with whether customers were actually helped. That gap — between what gets counted and what actually matters — became something I couldn't unsee. I watched it repeat across organizations and industries: activity metrics that look clean on a dashboard while the underlying reality quietly drifts away from them. That experience gave me a lasting commitment to measurement that reflects reality rather than distorts it.
When I discovered Knowledge-Centered Service, it felt like a methodology built around things I'd already come to believe: that the people closest to the work understand the problems best, that knowledge should be captured at the moment of work rather than handed down from a separate team, and that a healthy knowledge system is a living thing — not a static archive. Data science gave me the toolset to apply those ideas in ways that actually change outcomes.
What Drives My Work
I believe great knowledge systems live at the intersection of three things: how people actually behave, how information is structured, and how AI can amplify human expertise — not replace it.
Learning as a Way of Being
Learning isn't something I do in formal settings — it's how I engage with everything. Knowledge is always shifting, problems always have new angles, and every project is an opportunity to understand something more deeply.
Respect for Frontline Workers
The people closest to the work understand the problems best. Frontline analysts carry diagnostic instincts and pattern recognition that is rarely measured and often undervalued — my work is built on elevating that expertise, not overlooking it.
Measure What Matters
All throughout my career, I've seen metrics being gamed and abused at every level. Most organizations default to measuring what's easy rather than what's meaningful. I'm committed to building systems where the numbers reflect what's actually happening, not just what looks good on a dashboard
Knowledge as a Shared Asset
Knowledge only creates value when it flows. The goal isn't a polished top-down repository — it's an ecosystem where knowledge is captured at the moment of work, by the people doing the work, and continuously improved by everyone who uses it.
Foundations Before Features
Technology doesn't fix broken systems — it exposes and accelerates them. When the people and processes around a tool are aligned and functional, AI becomes a genuine force multiplier. When they aren't, it amplifies the dysfunction.
Ethical, Responsible Use of AI
AI should support human judgment, not override it. That means being honest about what models can and can't do, keeping humans in the loop on consequential decisions, and designing systems for trust rather than dependency.
What I'm Working On Right Now
- Completing my M.S. in Data Science capstone — a portfolio of applied ML projects focused on knowledge systems and support operations
- Restructuring KCS training into a self-paced, video-based experience aligned with a Structured Problem Solving approach