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Need a product person? Hire a scientist

One of the most consistent questions I’ve been asked over the years is where to find good product people (a term I use because nobody can seem to decide if they are product managers, project managers, or some other exotic variant). Most companies recognize the value that a highly-skilled product person can bring, so they are in high demand, and yet definitions of the role itself vary: It sometimes emphasizes deciding and scoping what will be built, sometimes managing the process of building, and sometimes owning the iteration back and forth between the two.

So unsurprisingly, when you look at the best product people out there, they don’t come from any consistent background. Some are former engineers or designers, others are subject matter experts, and some just seem to take to it naturally. But with a role so vague, it is hard to predictably find the right people to fill it.

And education isn’t helping. There are very few full programs that train product people and not even many good courses that do more than teach potential tools. So I’ve always struggled to answer the question, because if there is no consistent training and no consistent definition of what they should be doing, how can I possibly tell you where to find them?

Which naturally means I’m going to now try to do that. Recently, I’ve become increasingly convinced that the answer to Product is Science, and the answer to finding good product people is to look to scientists and those with training running experiments in a lab setting. There are a couple of reasons for this.

As I wrote briefly about recently, scientists are trained interventionists. The whole process of science is looking at the world as it currently exists and all we know about it, and then theorizing variations and potential outcomes. Which, I’d argue, is exactly what good product people should be doing. When you build a product, you are fundamentally trying to change some sort of behavior in the world. You hopefully have some understanding of what currently happens and why, what you want to happen and why, and then you experiment with methods that bridge the gap.

That’s exactly what scientists do on a daily basis. And they are highly familiar with the iterative process that it takes to get there and generally not frustrated by it. To have succeeded in science, they must necessarily have become accustomed to and patient with the universal truth that most experiments fail to produce the theorized outcomes (or often any discernable outcome at all). Behind every paper with a few studies in it, there are often a dozen failed experiments from which the scientist learned something about what didn’t work and then got busy creating another attempt at one that did.

And scientists are already trained to evaluate the data that helps in that iteration. While most are certainly not full-fledged data scientists, they are accustomed to looking at the statistical output of an experiment, evaluating the results, and then iterating to the next version. They can run regressions, are familiar with at least one statistical package, and don’t need to run off to a separate team to understand what worked and didn’t.

They are also accustomed to cooperation and advice, so when they do need help, they are likely to go get it. In academic science, it is virtually impossible to go it alone: The process as it currently stands requires an advisor. Rare is the entirely independent scientist, alone in a tower trying to resurrect their personal Frankenstein. Scientists are trained in a tradition of teams, with lab meetings, plenty of white boards, and discussion and debate. It isn’t a solo enterprise and that natural emphasis on collective action translates incredibly well into the modern development environment.

And you can actually hire whole teams because we already have lots of scientists. One of the benefits of discovering that we can repurpose scientists is that we don’t have to wait for the development, iteration, and subsequent years of operation of specific training programs for product people. While science certainly has challenges and we radically need to increase both the raw number and diversity of those that enter it, there is still a wide pool to draw from: According to the National Center for Educational Statistics, we get about 300K new grads a year with some form of science degree.

But perhaps more important than intervention, iteration, experimentation, data science, and abundance, we have orientation. Scientists understand the random. They won’t simply abandon a method because it doesn’t initially produce outstanding results. They’ll tinker with it, even run it again to make sure some outside variable didn’t interfere. Successful scientists, by definition, are those who don’t give up, who refuse to be discouraged.

Yet good scientists are in love with the problem, not the solution. They have only the lightest attachment to the experiments themselves and a strong passion for discovering the overall truth of the subject. I’ve never seen any group so ready to be flexible in method while holding steady on desired outcome, which is the literal meaning and whole point of a pivot.

And this is essential to good product. The number one reason companies fail is because they refuse to let go of product elements that are not driving the desired behavioral outcomes. Innovative products are by definition initially unknowable. The right solution must be teased out through experimentation and exploration. And to do that, we need trained tinkerers. To run product, we need scientists.

Side note: Yes, I fully recognize and embrace that one of the reasons I love the ideas of scientists as product people is because my background is science. But science is not without flaws. One of the reasons I left academia was because I felt (and feel) that academic science has lost sight of application. When I was shopping for PhD programs, the legendary social psychologist Tom Gilovich was the head of Cornell’s program and he called late one afternoon. “We’ve talked and we really want you,” he said, “but I just want to make sure that you’re clear on what we do here. You keep talking about applying psychology and we’re a research program – that’s what we do here, all day, every day.”

Tom saw something that I wasn’t yet mature enough to see, that despite my early successes as a researcher and love of expanding the knowledge of the field, I was never going to be happy with just research. My year as a Cornell PhD was one of the worst of my life and I was incredibly glad when it was over and I left to become Head of Product and Lead Scientist at Thrive. I probably needed that year to discover for myself the truth about a research-only focus but kudos to Tom, for calling it advance.


Also published on Medium.

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