Nobody told me a PhD in quantum physics would be useful in FinTech. I had to figure that out myself, mostly by watching the instincts kick in and then realizing where they came from.
The connection isn’t obvious from the outside. Physics is about particles and fields and mathematical structure. FinTech is about products, users, regulation, and revenue. The vocabulary doesn’t overlap. The training looks completely different.
But the underlying skills prove transferable: how you approach a problem, how you reason under uncertainty, what you do when you’re staring at something hard.
The Lab Trains You to Think in Systems
Physics is fundamentally about finding the coordinate system that makes a problem simple.
Most problems, presented naively, are complicated. The variables interact in messy ways. The dynamics are hard to track. But if you choose the right frame, the right representation and decomposition, the structure becomes clear. What looked like noise becomes signal.
You learn this not as an abstract principle but as a daily practice. You do it with differential equations. You do it with quantum states. You do it with experimental data. After years of this, it becomes instinctive: when you encounter a hard problem, the first question isn’t “what’s the answer?”. It’s “what frame makes this solvable?”
That instinct is unusually useful in business. Most business problems look complicated because they’re poorly framed. The team is fighting about tactics when they haven’t agreed on what success looks like. The roadmap feels impossible until you separate what’s urgent from what’s important. The organizational dysfunction resolves when you see that it’s a misalignment on ownership, not a culture problem.
I find myself doing this constantly. Looking for the right frame before trying to solve. Finding the representation that makes the structure clear.
Reasoning by Orders of Magnitude
In the lab, you develop a deep comfort with approximation.
When you’re deriving a physical result, you learn to trust order-of-magnitude estimates. You learn to throw away small terms. You learn to check your answer against your intuition about the right scale. Exact solutions are sometimes impossible and often unnecessary. What you need is to know whether you’re in the right regime.
This transfers directly to business decisions: the ability to reason in powers of ten, to know when a factor of two matters and when it doesn’t, to commit to an approximate answer when more precision wouldn’t change the decision.
Most people I work with don’t have this instinct. They’re trained to want precision, to build detailed models, to distrust estimates that don’t have many decimal places. The physics background gives you the opposite instinct, and in most business contexts, the physics instinct is the right one.
McKinsey Was the Translation Layer
After my PhD, I went to McKinsey. I didn’t go because I had a clear plan. I went because it seemed like the fastest way to learn how business worked.
The transition was harder than I expected, not intellectually but practically.
The lab trains you to be precise when business often needs you to be fast. To be rigorous when the situation calls for a judgment call. To present results with every caveat and assumption explicit when an audience needs a clear recommendation, not a paper.
McKinsey forced the adaptation. You learn to convert your thinking into something actionable quickly. You learn that a presentation with ten caveats is worse than a recommendation with one well-chosen caveat. You learn that the goal is to help a decision-maker decide, not to demonstrate that you understand the uncertainty.
The physics depth still came through: how I approached complex modeling, how I sanity-checked analyses, how I found the non-obvious structure in a messy problem. But I learned to apply it in the service of a concrete business question rather than as an end in itself.
Working on AML models and payment infrastructure strategy at McKinsey was where I first saw how directly the physics frame applied to financial systems. Payment networks have topology. Transaction flows have structure you can analyze. Fraud patterns have regularities you can model. The domain was new; the thinking tools were familiar.
What Fintech Needed That Physics Built
Building and leading a payments platform, the through-line became clear.
The payments infrastructure problem is fundamentally a systems problem. You’re designing interfaces between components that need to be reliable, composable, and resilient. You’re reasoning about failure modes before they happen. You’re building something that other things depend on, which means the architecture decisions are hard to reverse and the consequences of getting them wrong are large.
That’s not so different from designing an experiment. The precision that matters is in the design, not the execution. Getting the structure right early is worth more than moving fast later.
The same physics mindset applies to organizational problems: how to structure teams, where to put ownership, how to prevent platform teams from becoming bottlenecks. The question is always: what’s the right decomposition? What interfaces minimize coupling? Where does the symmetry in the problem help you?
I don’t describe it to colleagues in those terms. But that’s how I think about it.
What the PhD Didn’t Give Me
The physicist instinct also held me back in ways I had to consciously correct.
The lab trains you to present results with every uncertainty quantified, every assumption stated, every limitation acknowledged. That’s good science. In business, it often reads as indecision. Leaders who hedge everything don’t create conviction. Early in my career I communicated with too many caveats and not enough recommendation.
Physics also trains you to care about whether things are exactly right. That’s appropriate when you’re running an experiment where systematic errors matter. It’s not always appropriate when you’re deciding whether to invest in a new product area and the answer is “probably yes, with these risks”, not “yes, precisely, to four significant figures.”
Learning to match my desire for precision to what the situation required took time. I still catch myself doing too much rigorous analysis when a confident approximation would have served better.
The Background Is the Lens
Looking back, the path from atomic clocks to McKinsey to payments leadership wasn’t planned. But it wasn’t a detour either.
The PhD built a set of instincts that proved unusually applicable to the domain I ended up in: systems thinking, order-of-magnitude reasoning, comfort with uncertainty, looking for elegant structure in hard problems.
The question isn’t whether a non-linear background is an asset or a liability. It’s whether you learn to use it consciously.
The instincts are there either way. The choice is whether you apply them with awareness, knowing when the physics frame is useful and when it’s getting in your way, or let them run on autopilot and hope for the best.
I’m still learning when to lean into the physics instinct and when to set it aside. But I stopped apologizing for the background a long time ago, and the further I get from the lab, the more I understand what it gave me.