My Journey From Enterprise Analytics to the Future of AI

Building data-driven solutions and what's next

Does data run the world? Absolutely. But that data doesn't exist in a vacuum. Instead, it's an intricate material married permanently to the context in which it was created. Without that context, data is uninterpretable and without meaning.

At Chevron, I spent five years immersed in enterprise data, which forced me to think about the context in which it is generated for enterprise mobility. Now, with an MS in Data Science under my belt and a 4.0 GPA, and with my plan to pursue another MS in Software Engineering focused on AI Engineering, I'm ready to take what I've learned in the corporate world and build systems that help people.

The Numbers That Shaped My Thinking

Between five years at a Fortune 500 company and a master's in Data Science, I know one thing about data: it often hints at a story just waiting to be told.

As a Mobile Data SME, I designed more than 50 Power BI dashboards that helped over 50,000 employees understand how they were using mobile tools. Imagine that scale, 50,000 people, and your dashboards are the lens through which they see their own behavior. That's the kind of responsibility that sharpens your thinking real quickly.

But I also built AI chatbots with Microsoft Copilot and redesigned SharePoint systems to enforce stronger data governance across the organization. When you're working with that many users, governance is the backbone of everything.

Before that, as an IT Analyst from December 2020 through April 2024, I dug into mobility trends and presented findings directly to leadership. The key skill there? Learning to translate complex data into language that decision-makers could act on. I also redesigned onboarding processes using UX principles, learning to remove bottlenecks that had been frustrating new employees for years. Sometimes the biggest wins come from making things just work better.

And before I grew into those roles, I started as a Network BI Analyst, building real-time dashboards that tracked global network performance. Even as an intern, I was already thinking about how data could drive smarter decisions.

What I Built Outside the 9-to-5

The best part of this journey? I didn't limit myself to what Chevron had to offer. Since leaving the company, I have earned my degree and begun working on numerous personal projects that have piqued my interest.

I created an interactive resume powered by an AI agent, a chatbot built on Mistral AI that can answer questions about my skills, experience, and project fit. It's the kind of thing that sounds flashy until you realize how much it teaches you about prompt engineering, user experience, and the real limits of AI.

I also developed a policy framework addressing algorithmic bias, filter bubbles, and content moderation, as AI ethics is essential.

The Education Behind the Experience

Now, some people say you need to choose between real-world experience and academic rigor. I disagree.

I graduated from the University of Houston with a BA in Liberal Studies and minored in Business Foundations, Political Science, and Studio Art. Why is this important? Because I believe the best technical professionals understand context, culture, and communication. A strict technical background without the ability to tell a compelling story leaves half the work undone.

Then came my MS in Data Science from Boston University, where I focused on Machine Learning, Deep Learning, NLP, Statistical Modeling, and AI Ethics, maintaining a 4.0 GPA. And right now, I'm geared towards pursuing a second MS in Software Engineering with a specialization in AI Engineering at Western Governors University, starting May 2026. That program will teach me how to build production-ready, scalable, and maintainable ML systems. MLOps, software architecture, the whole picture.

I also completed graduate and undergraduate IT coursework at Liberty University, earning a 4.0 there.

The Tools I Work With

My technical toolkit includes:

Machine Learning & Data Science: Python is my daily driver for dealing with data: scikit-learn for modeling, TensorFlow for deep learning, Pandas and NumPy for data manipulation. I'm comfortable across the full ML pipeline.

Analytics & Visualization: SQL, Azure, Power BI. I've built hundreds of dashboards at this point, and I know how to extract meaning from data.

AI Tools: Microsoft Copilot Studio. I've shipped AI-powered chatbots into enterprise environments. I know what to do, and not to do, in production. I'm also proficient in Claude Code, Codex, Gemini CLI, Opencode Go, and Replit. Name the tool, and because I learn fast, I can deliver quickly.

Professional: Stakeholder communication, cross-team collaboration, and data storytelling.

Why I'm Looking Ahead

I'm hunting for roles in data science, machine learning, and AI, specifically those that let me build responsible, impactful systems. I've spent five years proving I can deliver at enterprise scale. Now I want to bring that experience to teams building the future.

Learning never stops, and I treat it as part of the job.

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Bottom line: I've done the work. I've built the dashboards, shipped the AI tools, written the models, and graduated at the top of my class, thrice (I also graduated with a 4.0 GPA from Houston City College). I'm ready for the next challenge.