Why I Returned to WGU as a Graduate Student (And Why I Don't Regret It)

Me, standing next to the WGU mascot Sage, at a graduation celebration.

After leaving Boeing in 2024, I found myself at a career crossroads that many software engineers face: "What do I want to do when I grow up?" The answer led me back to school—specifically, back to Western Governors University for a Master's in Computer Science with a focus on AI and Machine Learning. Here's the story of why I made that choice, how the tech job market influenced my decision, and whether I regret it now that I landed my dream job.

The "What Do I Want to Be When I Grow Up" Crisis

Let me be honest about something that took me years to accept: I never loved coding just to write code.

For the longest time, this made me feel like I didn't belong in the tech industry. I'd see other developers who could spend hours tinkering with code for the pure joy of it, while I needed a bigger purpose to feel engaged. I thought this meant I wasn't a "real" programmer.

I was wrong. What I actually love is the potential for impact that programming offers. One programmer can write code that gets used by millions of people. That scale, that reach—that's what excites me about software development.

As I processed my time at Boeing and started thinking about what came next, I kept coming back to one area that captured my imagination completely: artificial intelligence and machine learning. Everything about it was fascinating—how tiny changes could be amplified within these systems, how they could be applied across industries, how they were fundamentally changing the way we interact with technology.

The more I thought about AI and ML, the more I realized this wasn't just passing interest. My curiosity about these fields existed at all times, not just when I was actively working on related projects.

Following the Breadcrumbs: What Job Listings Told Me

As I began looking at potential career moves, I started tracking roles that genuinely interested me. I'm a hyper-organized person (thanks, hyperfocus ADHD), so instead of randomly applying to jobs, I created a comprehensive spreadsheet.

I tracked job titles, companies, required skills, preferred qualifications, and common keywords. The pattern became clear quickly: again and again, the roles that excited me involved AI and ML. Whether it was machine learning engineering, data science, or AI product development, these were the positions I found myself bookmarking.

It wasn't just coincidence—it was data telling me where my interests truly lay.

The Market Reality: When the Tables Turned

Here's something that dramatically influenced my decision: the tech job market completely transformed between when I started my career and when I left Boeing.

When I graduated and entered the industry in 2022, I was already coming in during a shift. The traditional "software engineer shortage" that had defined tech for years was starting to change. There was a regular flood of new graduates entering the industry, creating more competition and significantly harder interviews.

By 2024, when I left Boeing, the market had become incredibly challenging for engineers. Layoffs were happening across the industry, and the number of new graduates continued to increase each year. This created an insane number of applicants for every single software engineering position.

Previously, I had learned that women often only apply to jobs after meeting every single requirement on a job listing, while men apply with significantly fewer qualifications. Mentors had stressed to me repeatedly not to self-reject, because companies often hired candidates who didn't meet every criteria.

I had experienced this firsthand in fall 2023, when I applied to a Machine Learning Software Engineer role at Apple that wanted 1-2 more years of experience than I had. I got the interview, then a second interview, and then bombed the coding round. But the point was, I got in the door despite not meeting all the requirements.

After spending months applying in 2024 with none of the same reception, I realized something fundamental had shifted.

The Hiring Reality Check

Due to my involvement with FIRST Robotics and living in the greater Seattle area, I have many friends working in the tech industry. So I asked them directly: what had changed?

Every single person in a hiring or management position told me the same thing: the applicant pool had become so large that they were actually getting candidates who met their entire job posting wishlist. The days of hiring someone who met 70% of the requirements were over—at least for the roles I wanted.

This was the reality check I needed. If I wanted to compete for the AI and ML roles that genuinely interested me, I needed to figure out how to check more boxes on those wishlists.

Looking at my spreadsheet of job requirements, the most common qualification I was missing for the roles I wanted was a graduate degree. Not just any graduate degree—specifically one in computer science, ideally with coursework in AI and ML.

Why I Chose to Return to School

The decision to pursue a master's degree wasn't just about job requirements, though that was certainly a factor. I had found something I was genuinely passionate about in AI and ML, so returning to school to dive deeper into these subjects felt like a natural next step.

But why go back to school instead of self-studying or taking online courses? A few reasons:

Structured Learning: While I'm good at self-directed learning, I knew that having a structured curriculum would help me cover fundamentals I might skip if left to my own devices.

Credentialing: Fair or not, having a master's degree would open doors and give me credibility in conversations about AI and ML roles.

Time Management: I wanted to be strategic about my career transition. Ideally, by the end of 2027, I could have both a master's degree in CS with a focus on AI/ML and at least one more year of work experience. This would put me at a graduate degree plus three years of experience, firmly in mid-career territory.

This alignment was important because there was a mismatch between my years of experience (which classified me as early career) and my actual experience level (which was more aligned with mid-career work tasks). The master's degree would help bridge that gap.

Why WGU (Again)

Once I decided to pursue a master's degree, choosing WGU was relatively straightforward. They had launched a Master's in Computer Science with a focus on AI and Machine Learning—exactly what I needed.

But beyond the specific program, WGU was the perfect fit for my situation for several reasons:

The Competency-Based Model

WGU's competency-based learning model meant I could accelerate through material I already understood and spend more time on concepts that were new to me. Having done my bachelor's degree there, I knew this approach worked well with how I learn.

Time Flexibility

The flexibility was crucial. I wanted to keep the option open to work while pursuing my degree if the right opportunity arose (which it did). WGU's asynchronous, self-paced structure makes this much more feasible than traditional graduate programs with fixed class schedules.

Cost Considerations

Let's talk about something that significantly influenced my decision: money. College is expensive, and graduate school even more so.

I had accumulated $90,000 in student loan debt during two years of my bachelor's degree (one of which was even a medical withdrawal). After my husband and I worked hard to become debt-free, I was determined not to go back into debt until we buy a house.

This ruled out most traditional graduate programs, where tuition could easily run $40,000-$60,000+ per year. WGU's flat-rate tuition model meant we could afford to pay for my degree outright without taking on new debt.

Proven Track Record

I had already completed my bachelor's degree at WGU successfully. I knew the system worked for me, I understood the expectations, and I felt confident I could succeed in their graduate program.

The Plot Twist: Landing My Dream Job

Here's where the story gets interesting. While I was pursuing my master's degree to become more competitive for AI and ML roles, I actually landed what I consider my dream job before completing the program.

So the obvious questions are: Do I regret starting the degree? Do I still plan to finish it?

The answers are no, and yes.

Why I Don't Regret the Decision

I don't regret pursuing my master's degree at all, even though I found a great role before completing it. Here's why:

It was always something I wanted to do. While I started the program for strategic career reasons, pursuing graduate education was always an aspiration. I've loved learning my whole life and had always hoped to eventually earn a PhD (though that's probably not realistic given my other life goals).

The knowledge is valuable regardless. The coursework I've completed has genuinely made me better at my job. Understanding the theoretical foundations behind machine learning algorithms, diving deep into statistics, and exploring different AI approaches has enhanced my ability to contribute meaningfully in my current role.

It's future protection. The AI and ML field is evolving rapidly. Having a solid educational foundation gives me confidence that I can adapt to changes in the industry and continue working in this space long-term.

Personal satisfaction. There's something deeply satisfying about completing what you start, especially when it's challenging. I want to prove to myself that I can successfully complete a graduate program.

Lessons Learned About Career Strategy

This experience taught me several important lessons about career planning:

Data-driven decisions work. Tracking job requirements systematically gave me clear insights into what the market actually wanted, rather than what I thought it wanted.

Market conditions matter more than we'd like to admit. Individual merit is important, but it exists within larger economic and industry contexts that we can't control. Adapting to those conditions is part of career management.

Education can be strategic. Going back to school doesn't have to be about passion for learning alone—it can be a calculated career move that also happens to align with your interests.

Flexibility is valuable. Choosing an educational program that accommodated my other priorities (like staying debt-free and keeping work options open) was crucial for making the whole thing sustainable.

Multiple reasons make stronger decisions. My decision to return to WGU wasn't based on just one factor—it was the combination of program content, cost, flexibility, and proven track record that made it the right choice.

Looking Forward

I'm currently on track to complete my master's degree by the end of 2025, while working full-time in an AI/ML role that I genuinely love. It's a weird position to be in—I'm getting the education I wanted while already doing the work I was preparing for.

But that's okay. The education is making me better at the work, and the work is giving me practical context for the education. They're reinforcing each other in ways I didn't expect.

For anyone considering a similar path—returning to school for career advancement, particularly in a competitive market—here's my advice:

Be strategic about your choices. Understand what the market actually wants, not just what you think it should want.

Consider all your constraints. Time, money, family obligations, location preferences—all of these factors should influence your educational decisions.

Don't be afraid to make moves that look good on paper. Sometimes the "right" career move isn't the most emotionally satisfying one in the moment, but it positions you for future opportunities that are more aligned with your goals.

Remember that careers are long games. Individual decisions matter less than the overall trajectory you're building toward.

The tech industry will continue evolving, and so will the skills and qualifications it values. The key is staying adaptable while maintaining focus on your long-term goals.

For me, returning to WGU as a graduate student was about more than just checking a box on job requirements. It was about investing in my future, deepening my expertise in an area I'm passionate about, and positioning myself to continue growing in AI and ML as the field evolves.

And honestly? Even though I already landed a role I love, I'm excited to see where the combination of practical experience and continued education takes me next.

Are you considering returning to school for career advancement? What factors are most important in your decision-making process? I'd love to hear about your own journey—connect with me on LinkedIn or follow @code_with_kate for more insights on navigating tech careers and education.