April 13, 2024


Sapiens Digital

Three Reasons Every Tech Startup Should Consider Adding A CSO To Its C-Suite


Dr. Bob Lindner, co-founder and chief science officer, veda.

When most of us hear the word “science,” our minds go to astronomy, chemistry and physics—the physical sciences. Similarly, when we hear the title “chief science officer,” or CSO, we either don’t know what the role entails or we automatically make an association with organizations focused on research, the life sciences or pharmaceuticals.

But given the huge potential benefits of applying the scientific method to other industries, such as healthcare, I predict that over the next few years, we’ll see companies in many verticals hiring for the CSO role and building out data science teams to support this position. In fact, I believe that in today’s environment, tech startups specifically can derive a great deal of value by adding this type of role. I’ll use health tech as the case study for this article because that’s the industry I’m in, but these learnings are applicable across industries.

What Does A CSO Do?

In my initial role as CTO, I was responsible for engineering our enterprise system for reliable performance and scaling. When I returned to my scientific roots as CSO, my focus shifted to taking authority over the processes our company uses to gain information. In a nutshell, this means having an idea, examining what the data says about the idea, hypothesizing what can be done to change that data if needed (e.g., spotting errors and preventing them from reoccurring by teaching the technology to recognize them next time), testing the hypothesis, and then re-measuring to compare and trying again as needed. As a result of our training, scientists are patient enough to follow all these steps and present findings in a way that’s easy for other business decision-makers to comprehend.

While there was a point in time when many business decisions were based on instinct, past experiences and, yes, sometimes even ego, today, technology vendors are being tasked with proving our outcomes. It’s the perfect time to invest in the CSO role and bring the scientific method to bear in business.

Here are three reasons to bring on a CSO:

1. Back Claims About Your Technology With Data To Build Trust

Today, every dollar a business spends gets scrutinized, and every dollar spent must be justified. The expectation is that when technology partners demo our products to prospects, we not only enumerate their benefits to potential buyers but also fully substantiate any claims we make.

The scientific method—which is, at the most basic level, the process by which humans can discover information about the world—is already being deployed across the healthcare industry by certain companies to this end (e.g., in diagnostics and medical devices). These companies are asking questions like: How do you know when you know something? How do you design a process to learn about something? How do you know if you’re making the right choice based on data?

Truly all health tech companies should be treating the performance of their products with the same level of rigor that exists in these other areas of healthcare—even if their end user isn’t a patient. Health tech companies rooted in science can actually write accuracy guarantees into their contracts. We’ve been able to do that at Veda, and it makes all the difference in the world. The trust built by making a promise on paper endures far beyond the sales cycle.

2. Move Fast—Without Breaking Things

With all the VC funding in the health tech industry over the past few years, it’s no wonder that so many startups feel intense pressure to move fast. And now that the VC bubble is starting to burst and investment dollars aren’t quite as easy to come by, the pressure may mount. But in the absence of a gatekeeper role like the CSO that is tasked with maintaining the integrity of data that is presented in support of a product’s value, companies run the risk of unintentionally breaking things. In some extreme cases, startup executives have purposefully manipulated their data to show desired, rather than actual, results.

One of the most egregious examples of this is Theranos, where company executives were accused of deleting quality-control data and cherry-picking data to characterize their lab tests as reliable, when in actuality, a full view of the data would have shown otherwise. Another example is Purdue Pharma and its characterization of OxyContin as a non-addictive opioid. Purdue’s marketing materials, which were put in front of thousands of doctors, manipulated data from studies on the drug and misrepresented its safety profile.

The very antithesis of a contractual obligation to maintain data accuracy, these stories illustrate the harm that can come from moving too fast. Scientists aren’t always the loudest voice or flashiest personalities, but it’s really important that they are part of the executive team to hedge against preventable business mistakes and retain the credibility of health tech ventures.

3. Grow Your Total Addressable Market

I’m sure some readers will wonder if adding a CSO could slow down a company’s progress too much. In reality, leveraging the scientific method in industry actually allows many companies to expand their total addressable market and accelerate progress. A CSO drives the development of rapid prototypes as well as new capabilities and technologies. Their team is tasked, in essence, with running constant experiments. Just like in science, they design objectives and reliable systems to decide if they will pursue them or not. In the face of unexpected results or new data, they are trained by education to pivot and either adjust the current experiment or design a new one.

In health tech startups, specifically, there are never enough resources, so these companies can use the scientific method to reliably, quickly and efficiently decide whether to pursue potential new business developments without relying on external resources for validation.

Over time, I predict that the health tech sector and beyond will apply the scientific method more concretely to its everyday business processes and the AI and ML data science “hype bubble” will inevitably burst.

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