Scientists: You Do Have the Tools to Make the Jump to Business

Scientists are nerds even in their own heads and many can find it difficult to see how their skills translate to the business world. Further, popular culture suggests that they are the last thing you want around when you have to explain things to normal people. People laugh at the Big Bang Theory. Anyone remember Tom Symkowski interviewing with The Bobs?—“I deal with the g--d--- customers so the engineers don't have to!!”[1] While scientists are hilarious,[2] looking for your first job out of science is more of a drama.  This article is for the benefit of the scientist preparing a non-science resume and (who are we kidding) the HR professional who may review it. Let’s take a look at some of the things scientists learn that make them great business people.

The root cause of the bumbling genius stereotype driven by pop culture is that scientists are literally trained to speak differently. The language of science revolves around technical terms which have very specific meanings. Conversely, the language of business has words with more fluid meanings. Although the business world is quantitative—the bottom line matters—discussions can meander down non-quantitative and emotional paths. However, many executives and successful business people at all levels are those who can context switch between quantitative and non-quantitative contexts. For example, “Great commercial! How many more widgets have we sold since it aired?” Scientists have the quantitative skills and, if they can learn to be comfortable with incomplete and uncertain information, the non-quantitative skills as well.   

Consider that the number one business executive skill is keeping your eye on the ball. When the staff gets caught up in minutia or starts to worry about ancillary goals, good executives step in and refocus efforts with aplomb. Behind that aplomb, the executive has, through experience or calculation, identified what was important to the company and how to measure it. Scientists do this all the time. They learn to identify and isolate driving factors from a myriad of confounding variables. They know how strongly or weakly the result depends on each of the variables. In science-speak, these are your approximations in a certain limit, Taylor series, or just knowing the equations behind the result. In the business world, you keep the math behind the scenes, do the approximations in your head, and lead with confidence—unless you are talking to the finance quants. 

Within this ability to know what’s important and what isn’t is the single most useful science skill applicable to business: seeing the math behind the words. Consider the following scenario.  Two business colleagues are arguing over whether (1) lowering advertising costs or (2) lowering supplier costs will boost profits the most.  The scientist-turned-business-pro lets them know they’re both right.  He asks, translated to business-speak of course, if the signs of each term are right given the current ad budget and supply chain conditions. Then, what is their data showing the magnitude of each term? Almost all discussions in business are over word problems where the governing equation is never explicitly stated by business people. Scientists rock at pulling out explicit mathematics through the noise.[3]

In addition to keeping their eye on the ball, good business leaders don’t panic over small stuff and they react without hesitation to the big stuff. The market just made a big move. The Chinese supplier doubled the cost of widgets. The VP of Gadget Quality just quit and took ten folks with him. Report these to a good executive and he either looks unfazed or immediately calls the CEO’s scheduler. Scientists, the skill those executives are using is scale. HR representatives, one of the first questions a scientist learns how to answer is “Big or small compared to what?” Quantitatively understanding what really matters is crucial in business. Scientists understand scaling arguments: even at double the price, Chinese widgets are still cheaper than American widgets and our overall product cost increases 0.02%. No sweat.       

Another thing good business people do that scientists also do well is to recognize statistical fluctuations and trends. Scientists learn statistics very early in their careers and apply them to experiments to verify discoveries. Concepts like variance, time-windowed (“rolling”) averages, correlation, and non-Gaussian distributions are well understood by scientists. In business, prices, sales, and other things are always fluctuating. Business people are always asking questions like is our product performance reliable? To what extent? How many tests are necessary to guarantee performance, or how many samples must be checked to guarantee quality? If something unexpected is happening, is there something wrong with the way it is being measured? Scientists are adept at the statistics needed to answer these questions. The proverbial “factory floor” and the laboratory floor aren’t so far apart.

Let’s take statistics even further. Behind every statistic is a model. A model contains the knowledge that lets you predict things. Business people make it a priority to know the business climate: consumer demand, interest rate forecasts, tech-refresh cycles, continued buying of people-driven cars, etc. When is a change in sales just a blip? When is it a market stampede to a disruptive new product? If the business climate changes and the knowledge and assumptions you were using to predict things becomes invalid, companies that cannot adapt their business models go bankrupt. 

In science, the “business climate” is a model analogous to the standard model of particle physics, the ideal gas model, or cell models. Scientists are well trained to understand when models are failing and to understand the limitations of models. When you measure something unexpected in science, theorists dash off to build new models to explain it and predict wholly new things implied by the new data. In business, you win big if you understand the future business climate before your competitors do.     

Putting models to practice, consider that leading change in an organization is a top executive quality because change is risky and difficult to implement, but sometimes very necessary. If the data supports the need for change—it is a stampede to a disruptive new product—the CEO may have to go the board and recommend a totally new business model to survive. For example, consider a business that has been building and installing custom-fit tubs for years, but 3D printers are now entering the marketplace.  If the CEO’s argument is backed up by her scientist-turned-business-pro’s analysis that shows home-based 3D printers will kill the demand for custom tub factories, but the company has institutional knowledge about materials, there might be time to re-tool and survive selling materials for 3D printers. I bet a lot of big box stores wish they had more data-driven change leaders on board when Amazon struck their book-seller colors and raised the Jolly Roger as a logistics and data company.  

Finally, let’s directly challenge the nerd notion about scientists. Scientists do much more than just math. Starting as students in seminars with famous and intimidating names in the audience, they learn to ask direct and challenging questions of authority. They have to pitch ideas to get funding and 10% is considered a good win rate! They choose the things in their field to work on and their careers depend on how significant those things turn out to be. Every new investigative avenue is a science Start-Up.  They have to be competitive and work on deadlines to scoop the discovery and publish first. Early in their career they have to choose an advisor. Later they have to recruit students and junior scientists.[4]  If a scientist has ever been in a leadership role like leading a large research group or being a Department Head, they have the people skills to manage and influence others who have huge egos and a great deal of autonomy.  That’s a good skill for dealing with board members. 

Scientists, don’t sell yourselves short. I’m sure many of you would never think of leaving science. However, there are more roads off-the-tenure-track than on it. Even if you’ve got tenure, maybe a Chief Technology Officer job in a hot industry sounds good. Just remember that your business resume is not your C.V. Think about how your thinking applies to the job you’re seeking and show them how your thinking can be a good fit for a company. It’s not the publication or the result that matters, it’s how you got there. Did you convince the Dean’s Office to fund a new laser lab that led to a Nature cover and poaching a Nobel laureate from Harvard? Ok, ok, that guy has head hunters calling him. But you junior scientists, have you sought grants, developed new experimental techniques, derived an equation, found the guy in the math department who actually understands block chains, figured out buying gold from the pawn shop is cheaper than from the science supply house? There’s a proposal preparation, new product, new analysis framework, team building, and supply chain story in there somewhere. To conclude, you have the tools if you want to make the jump. Build your resume using plain language and show the HR person all the skills that supported getting to those fabulous publications. 

[1] Engineers can be mocked by scientists, but for the masses they’re both math nerds. 

[2] See, The Far Side

[3] Note this skill is also useful in dismissing political “talking heads” who blither on incessantly about some policy that will obviously improve life, but with no data on whether the improvement is offset by other effects.   

[4] They must build good teams because the junior folks do the real work—just like business.