About Gina Aulabaugh

AI Governance Researcher · Data Scientist · Systems Builder

I build systems that detect when AI is producing the wrong thing.

I spent a decade embedded inside one of the largest US telecom retention operations watching metrics improve while customer outcomes declined. Most organizations never find out why — because the data that would reveal the problem is the same data they're using to measure success.

So I built the instrumentation to catch it.
That work produced the Trust Signal Health Framework, the System Integrity Index, and the AI Code Integrity Auditor. As a late-diagnosed AuDHD adult, I think in systems — and I see the structural failures that standard analysis walks past.

The 42.1 percentage-point gap was not a theory. I lived inside it for ten years before I built the framework to prove it.

How I Work With Data

I work with data using a structured, decision-centered approach. I start by clarifying the business question, validating what the data can (and can’t) support, and choosing methods that fit the context—exploratory analysis, regression/classification, or feature engineering.

I treat data as a product of real systems—people, incentives, tools, and processes. When results look “off,” it’s often not the analysis that’s wrong, but the assumptions, definitions, or reward structures behind it. My goal is insight that holds up in real-world decisions—not just a cleaner dashboard.

Skills & Tools

Applied Analytics

• Explainable Analytics & Decision Support
• Hypothesis-Driven Analysis
• Predictive Modeling (Regression & Classification)

Data Preparation & Quality

• Data Cleaning & Wrangling
• Feature Engineering
• Data Quality Assessment
• Reproducible Analytical Workflows

Frameworks & Thinking

• CRISP-DM
• Systems Thinking
• Decision-Centered Analysis
• Ethical & Responsible Data Use

Tools & Technologies

• Python (pandas, numpy, matplotlib)
• SQL
• Jupyter Notebook / Google Colab
• Git / GitHub
• RapidMiner

Extended Bio

I build decision-ready analytics that hold up in real operations—predictive modeling, data quality checks, and reproducible workflows that turn noisy data into usable outputs.

Before formalizing PixelKraze, I spent over a decade in high-volume, high-stakes customer environments where accuracy, clarity, and trust matter. That background keeps my work outcome-focused and practical about metrics, incentives, and how reporting can drift from reality.

PixelKraze is where I publish that work—a portfolio of applied analytics projects, audit-ready artifacts (receipts, validation checks, run logs), and actionable outputs.

Quick facts

Location:

Ringgold, GA

Focus areas:

CX, retention/churn, ops, data quality

Open to:

projects, feedback, client work

View Resume

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Copyright & Licensing

All original content, models, documentation, and frameworks on this site are the intellectual property of PixelKraze, LLC unless otherwise stated.

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

Commercial use, redistribution for profit, or incorporation into proprietary systems requires prior written permission.

Independent work using synthetic or public data. Not affiliated with or endorsed by any employer.

Contact: Gina.Aulabaugh@PixelKraze.com
Contact: Gina.Aulabaugh@PixelKraze.com