MSA Alumni Spotlight

Shannon Kehoe (’19)

Head of Innovation, QuantPi

Frankfurt, Germany

 
   
      Shannon Kehoe    
 

About Shannon

I graduated from Georgia Tech in 2015 with a Bachelor of Science in International Affairs and Modern Languages, concentrating in German and minoring in Economics. I’m originally from Philadelphia. I actually discovered my passion for programming and statistics through my economics courses. I realized that data isn’t always the loudest voice in the room, but it’s essential for making better decisions and real-world impact. That idea—using data to make things better—has been central to every role I’ve taken since.

After undergrad, I pivoted into market analytics and worked at The Home Depot and Koch Industries before returning to Georgia Tech in 2017 to start my Master of Science in Analytics, focusing on Analytical Tools. I completed the program in 2019, finishing my practicum in Germany—and I’ve been living in Frankfurt Rhine-Main ever since.

If anyone is curious about building a career in Europe or navigating international data roles, feel free to reach out — I’m always happy to support MSA students.

Shannon Kehoe’s Journey from Georgia Tech to Germany

How does a Georgia Tech MS in Analytics degree open doors across the globe? For Shannon, it meant transforming a passion for data into an international career — from The Home Depot to leading innovation at a deep tech startup in Frankfurt, Germany.

In this episode, she shares what it takes to bridge the worlds of business and technology, why patience and adaptability matter, and how “reliable beats perfect” when building scalable systems.

How do you use analytics or data science in your day-to-day work?

In my recent role as Head of Innovation, analytics was central to everything I did — from identifying emerging technologies to testing product-market fit. I used data to guide decisions around which ideas were worth pursuing and how to measure their impact. Whether it was modeling system reliability or evaluating user engagement, data provided the foundation for smarter, evidence-based decisions that helped the team focus on what truly added value.

What do you enjoy most about your job, especially when it comes to working with data?

What I enjoy most is seeing how data can bridge the gap between technical teams and business leaders. It’s incredibly rewarding to translate something highly technical — like a machine learning model — into insights that drive real-world impact. I love that “aha” moment when both sides understand how the work we do makes products better and ultimately helps people.

What's one favorite memory you have from the MSA program-academic, social, or professional?

One of my favorite memories was working late nights in the ISYE computer lab with classmates like Liz Stanford and Dakota Hustler. It wasn’t just about the coursework — it was the camaraderie, the shared struggle, and the humor that kept us going. I even had my mom fly down for two weeks to help me manage everything while I was working full time and studying part time. Those small moments of community really defined the experience for me.

What MSA skill, class, or project helped you get your job or grow in your career?

The biggest impact came from Reliability Engineering. That class completely changed how I think about building systems — not just software, but organizational processes too. It taught me that the goal isn’t perfection; it’s creating something stable, scalable, and predictable. That mindset has been invaluable in my work leading technical innovation teams, where the challenge is balancing experimentation with reliability.

What advice would you give to a MSA student looking to break into your industry?

Be patient — and know your audience. You’re uniquely positioned to understand both the technical and business sides of a problem, and that’s a huge strength. But it also means you’ll often need to translate between the two worlds. Learn to speak both languages fluently and adapt your communication style depending on who’s in the room. That’s how you build trust, collaboration, and influence. Don’t shy away from differences of opinion — that’s how you catch issues before they make it to production. Good collaboration between business and tech teams is what makes data work reliable and impactful.