People are often surprised to learn I didn't go to business school. I have a PhD in biochemistry. I did a postdoctoral fellowship at the NIH studying aging and inflammatory disease. My training was almost entirely in science.
And yet, running companies turns out to be something I was prepared for in ways I didn't fully recognize until I was doing it.
The skills that make a scientist effective in a lab are not separate from the skills that make an operator effective in a business. They're the same skills. I build protocols. I test hypotheses. I train people and run experiments. I track data, monitor what's working, and adjust based on what I observe rather than what I expected. I can break complex goals into tangible, sequential steps and hold a team accountable to each one.
Nobody told me that was a business education. But it was.
The Protocol Mind
In science, a protocol is the documented set of steps you follow to run an experiment. It's not optional and it's not approximate — it's precise, it's reproducible, and it exists so that anyone on the team can execute the same process and get comparable results.
Most businesses don't operate this way. They run on institutional knowledge: things people know because they've been around long enough to absorb them, undocumented and unverifiable. When those people leave, the knowledge leaves with them.
A scientist builds protocols instinctively, because reproducibility is the entire point. You can't know if your results are real if you can't recreate the conditions that produced them. I brought that same instinct to every business I've built: SOP-first, documented before it's delegated, auditable by design. It's not extra work. It's what makes scaling possible.
You can't know if your results are real if you can't recreate the conditions that produced them. That's true in a lab. It's equally true in a business.
Hypothesis Testing Instead of Opinion Following
When a scientist has a question, the answer isn't to poll colleagues or trust intuition — the answer is to design an experiment. You state your hypothesis, you control your variables, you run the test, you evaluate the data, and you update your understanding based on what actually happened.
Most business decisions get made the opposite way: someone has an opinion, they advocate for it, and the most persuasive or most senior voice wins. This is an extraordinarily inefficient way to find out what's true.
Testing is faster and more honest than debating. (Harvard Business Review found that startups using the scientific method generated significantly more revenue than those that didn't — and were more likely to pivot away from ideas that weren't working.) Not every business question lends itself to a controlled experiment, but far more do than most founders realize. A/B test the landing page. Run the campaign in one market before rolling it out nationally. Try the new pricing with a segment of clients before changing it across the board. Let the data have an opinion before you finalize yours.
The scientific mindset also helps with the emotional side of being wrong. In science, a failed hypothesis isn't a failure — it's a result. It tells you something real about the world. That reframe matters enormously in business, where founders often avoid running tests because they're afraid of what the data might say. Being wrong faster just means you get to being right sooner.
Monitoring as a Discipline
In a lab, you don't run an experiment and walk away. You monitor. You watch what's happening, track your measurements over time, look for unexpected patterns, and stay alert to results that don't fit your model. The monitoring is where the learning actually happens.
Founders often build systems and then stop looking at them. They launch a product and check the metrics once a quarter. They hire someone and assume things are working until they're obviously not. This is the business equivalent of running an experiment in the dark.
Consistent, disciplined monitoring of revenue, operations, team performance, and customer behavior is what separates founders who are reactive from founders who are ahead. You're not looking for problems after the fact. You're watching the data tell you where things are trending before the trend becomes a crisis.
I still design and run clinical studies at SOM Aesthetics. The monitoring discipline I built in a research lab is the same one I apply to the business. The instruments are different. The mindset is identical.
Training People Like Scientists Train Technicians
In a research lab, you train your people carefully — not because they aren't smart, but because the work requires precision and the stakes of imprecision are high. You don't assume someone knows how to do something because they're capable of figuring it out. You show them the protocol, you run them through it, you verify their technique, and you check their work until you're confident they've got it.
This approach to training feels slow until you realize how much time most businesses lose to errors, rework, and confusion that stems from insufficient onboarding. Training well upfront is a significant time investment that pays back in multiples: in quality, in confidence, and in the ability to delegate cleanly.
It also respects the person being trained. Clear expectations and documented protocols aren't micromanagement. They're a gift. They let people do their job without having to guess what good looks like.
Breaking Complex Goals into Steps That Actually Move
Research goals are almost always enormous in scope and distant in timeline. A PhD dissertation is a multi-year project with a result that's uncertain by design. Learning to operate inside that kind of ambiguity. Finding the next concrete, executable step when the destination is still foggy is something scientific training forces you to develop.
It's also exactly what founders need. The vision is always bigger than the current capacity. The gap between where you are and where you're trying to go is always uncomfortable. The skill isn't seeing the whole staircase. It's identifying the next step, taking it, and then finding the one after that.
I break big goals into protocols. I track progress against milestones. I monitor whether the steps I'm taking are actually moving the needle, and I adjust when they're not. It's not sophisticated strategy. It's just applied scientific method, and it works the same way in business that it does in a lab.
What This Means If You've Never Trained in a Lab
You don't need a PhD to think like a scientist in your business. The underlying practices — documented protocols, hypothesis testing, consistent monitoring, disciplined training, iterative progress toward a long-range goal — are learnable habits. They're just not the habits that most business advice encourages.
Most business advice encourages speed, boldness, vision, and moving fast. All of those things matter. But the founders who sustain results over time are the ones who build the infrastructure underneath the speed — the protocols, the data, the systems that let them move fast without losing their grip on what's actually happening.
Work smarter. Build the systems. Trust the data. Adjust when you're wrong.
Turns out, that's science. And it's also how you build a business that lasts.