Making a difference with tech
By Diana Murgulet – I was born in a small town in the heart of Transylvania, Romania. From an early age, I was drawn to how things work—how logic and creativity come together in technology. That curiosity took me to the UK to study computer science and machine learning.
I first heard about QuantumBlack, AI by McKinsey, at a networking event where I struck up a conversation with someone who worked there. It sounded really cool—exactly the kind of place where I could grow and contribute—so I applied. A few interviews later, I joined as a junior data scientist.
After four incredible years, I left to pursue an MBA at Oxford, hoping to broaden my perspective. A year ago, I returned to QuantumBlack—this time with a stronger sense of how tech, business, and impact can intersect.
My experience cofounding a nonprofit before my MBA with my colleague and friend Viktoriia also contributed to that. Our organization, Doobeegood, was born of our shared passion for education. We launched our first summer camp in 2022 to support Ukrainian refugee children; since then, we’ve expanded to include Roma kids as well and have supported over 300 children in total.
Our goal is simple but powerful: equip kids with the tools to thrive in today’s world through fun, interactive, and cutting-edge learning methods. We design immersive summer camps that blend STEM, the arts, and sports. We bring in high school students to demo robots they’ve built, and we run AI and coding classes and teach kids how the apps they use every day actually work. It’s tech as a tool for wonder, growth, and confidence.
Navigating AI in life sciences
In my day-to-day role at McKinsey, I help life sciences organizations figure out how to unlock the power of their data and integrate AI into their work—without losing sight of standards, accuracy, or impact.
These companies are managing enormous data sets in highly regulated industries. The pace of change is dizzying. My job is to help them make sense of the complexity and integrate technology in ways that align with their goals and values.
Generative AI, in particular, offers exciting potential to accelerate work—but it doesn’t always match the predictability of traditional machine learning. That’s why co-creating solutions with clients is so important. We put the right guardrails in place and make sure everyone understands both the possibilities and the limitations.
QuantumBlack, AI by McKinsey
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AI as a force for good
A lot of conversations about gen AI focus on risks—and yes, those matter. But I’m most fascinated and energized by the good we can do. I’m especially excited about the potential to better understand diseases and speed up drug discovery. We still lose far too many lives to preventable diseases, often because the right drug doesn’t exist yet, or it takes too long to bring it to market. AI can change that. And when it does, the impact will be profound and game-changing.
To me, technology should always be in service of something greater: social good, climate action, better education, or equitable healthcare. I believe we can move fast and move forward with technology—without leaving humans behind. And on a more personal level, AI has made my work more fulfilling. I can scan data and write code faster, which means I get to spend more time on what I love: cracking complex problems and helping organizations find real breakthroughs.
That’s what makes McKinsey so special. We take some of the hardest problems in the world and we solve them together with our clients to positively impact their customers, their people, and society as a whole.
Challenging assumptions, sparking breakthroughs
Things get really interesting when the data tells a story that contradicts client expertise or long-held beliefs and practices. When it contradicts client expectations, the easy answer would be to question the data. But once we’ve triple-checked everything, the real conversation begins.
Building and maintaining client trust is essential. It’s not just about presenting a model or pointing to a dashboard—it’s about working side by side to understand what the data means and what it’s asking us to reconsider.
A lot of data science involves detective work: forming hypotheses, testing them, digging deeper. But sometimes, the most honest—and most powerful—thing we can say is: “We don’t know yet.” That’s the moment real breakthroughs become possible. Because the goal isn’t to have all the answers. It’s to build them—together.