Nathan Clark, Ganymede | Founder Interview

In this From the Founders session, we talk to Nathan Clark, the co-founder of Ganymede. Ganymede is a whole-lab automation and data integration platform that allows you to connect any lab instrument with any app or pipeline in one simple, low-code platform. Read on to learn about how Nathan went from being a bond trader, to a PM, to starting a biotech company.

E: To start us off, can you give us a quick introduction of yourself and your company?

N: I’m Nathan. I’m the co-founder of Ganymede. We’re creating a true, basic computing cloud platform, but specialized in the sciences and more broadly, anything where there’s complex physical processes going on. For example, manufacturing as well.

We’re very good at connecting to hardware, like lab instruments or manufacturing machinery. We’ll get the data off and sink it, and then build this pipeline and data science environment on top of it that allows people to build analysis, automate that sort of data analysis, build databases out of it, then push data into tools like Benchling — or, in industry terminology — electronic lab notebooks. We cater primarily to engineers and data scientists in the space. There are also many companies in biotech that are really just scientists and are not that technical from a software perspective, so we’ll build on top of the platform for them to build these integrations.

We launched earlier this year in February but have grown extraordinarily quickly. We started hiring, and out of our founding team of four, other than myself and my co-founder, the other two came from AngelList Talent. We raised our seed round, then our series A. Now we’re at about 15 employees.

E: Sounds like it’s been an eventful year for you — what have been some of the biggest turning points for the company that you felt were defining moments?

N: Because of what we do, we have very large client contracts. Our contract value is over six figures ARR per year, which, for a startup of our size, is pretty good. The turning points as a result have really been just seeing us sign those client deals. We’ve been able to prove that even though we’ve built this very quickly, it works and it’s successful for clients to the point that we can sign these deals and even grow them already. But that’s a turning point that’s enabled by having the right team.

Andy, our Head of Engineering, quickly became a very strong engineering manager. It was a bit unexpected because we had thought of him as an IC engineer when we hired him — but now, because of him, I don’t think about the engineering team at all. I know Andy’s got it. I don’t code at all, which isn’t something I had expected. And now, we’re scaling up, because I get to focus on the product, sales, and commercial side. The fact that I’m not going in and having to work with tickets or trying to help the engineering team orient on technical things, and instead get to be purely focused on generating product requirements and selling clients, that’s amazing. So that was another general turning point in addition to us signing our first client.

E: I’m sure signing your first client must have been an incredible moment as a founder. Do you recall the exact moment?

N: Yeah, there’s one moment I can call out with one of our clients that we ever really realized we could do business with. It was this company called Apprentice, a manufacturing execution system. They’re also a startup themselves, but they’re bigger and were more serious. We were talking to them just trying to learn about the industry generally, and then we realized they could use Ganymede. The CEO suggested we talk to their integrations director and try to figure something out. It was the first moment we started to realize we can actually start building here, so that was very cool. We were standing outside their Jersey City office and just realized, “This is a thing. I need to start recruiting.” We had just started the company and properly left to do this full-time and realized in that moment we needed to jump on this, start hiring, and start building.

E: It sounds like it all happened really fast for you. What was your background like leading up to this?

N: I started as a bond trader originally in my career and to this day, still love finance. It’s an extremely interesting space. I then joined Affirm as one of the founding members of their capital markets team, then later moved into product management across new financial products, and many other products. I met my co-founder there, and we had both always been passionate about biology and wanted to do more there. We’d do some things on the side and volunteer in some academic things, but I’d never actually worked in the space.

When Affirm IPO’d, we decided that we wanted to go into biotech properly and learn the space. I went to Benchling and was the product lead there for their data products, so the insights BI tool, their data platform, machine learning team.

E: How did the transition into starting your own company happen? Did you always know you wanted to be a founder, or had you just come across this idea that was really working and you ran with it?

N: I think, though, that it’s hard to get the leverage to make the impact you want without being a founder. I’ve been in unicorn kinds of companies, and you would think maybe there would be a little more of revenue to say, “let’s use the unicorn company to make the impact”. But the problem I feel like with those companies is that once they’re past the series A, B, C stage, it’s kind of incumbent upon them to be maximizing their major revenue source pipeline and not always necessarily investing in crazy new ideas. I think I’ve always chafed at the fact that I go into some of these companies and want to do crazier, broader things, and it just doesn’t get prioritized. So that’s a lot of what led me to just doing this myself eventually and realizing there’s no way to get the leverage and the level of freedom you want without doing it yourself.

Also — and this is not necessarily as much true at the moment — but still, even to an extent, if you really believe in what you’re doing and you’ve put hard work into it and thought about it seriously and know that there’s a commercial angle to it... you will not struggle for funding, even now in 2022. I think that’s the other aspect of this: that there’s nothing stopping you from being a founder other than being willing to take that risk, which I’m very fortunate in my life where I’m able to do that pretty comfortably. Not everyone is, but once you’re ready to do that and make that jump, it just works.

E: You talked about how being a founder was the route to having the level of impact you wanted to make in the space. Can you tell me more about that? When you think about the vision of Ganymede and the impact you want it to have in the world, what does that look like?

N: I think the real thing that’s going on in biotech is that drug prices are very expensive. If you say that making money is a proxy for doing good in the world, I think biotech is a broken space because the drug companies are making plenty of money. What we really need is to improve the velocity of drugs coming out, which will cheapen those drug prices and remove, to some extent, cash from the big pharmas. And by extension, increase the dividends, and pay the innovators and scientists and early-stage people who are very underpaid.

You see people in fintech or with a high-end software engineering background where they’ll come out of college and make 140K as a salary. Then you look at these scientists who have PhDs and they’ll come out of their PhD program making 100, 110K and that’s it. That disconnect, I think, is a function of the fact that scientist’s work is very menial. It’s very hard. There aren’t really “10X scientists” and in a lot of ways, it’s too hard because you’re spending such a large percentage of your time doing the same things, like pipetting things around what everyone else is doing. The answer is, yes, we need automation obviously, but the automation is also very slow to the industry and the hardware is there.

The impact that Ganymede wants to have is to accelerate scientists at the lab bench by better describing their work. Align more of it to be automated and therefore allowing them to spend more of their time focused on drugs and scientific innovation, which will, as a proxy, increase their pay and increase the output of drugs going to the industry. That’s what we call ourselves on: how much can scientists be paid in the biotech industry? And then overall, just the volume of innovation of drugs and therapeutics coming out.

E: You’re at an interesting intersection between your background in tech, finance, and this interest in biology. The tech and finance side make sense to me, but I’d love to hear where your interest in biology came from.

N: I like academic finance because it’s scientific in a lot of ways, because it’s built on human construction. It’s very satisfying and mathematical, at least in some domains. There’s a lot of value to being quantitative. Biology is almost the exact opposite, and I dislike it as a science. I’d say that academically, I don’t like biology. But there’s something mysterious about that. Why is that? Why is biology an annoying science? I think it’s because human evolution is messy and people are doing R&D here to try new things. It makes it very hard to pin down any real structure or theory to it.

Biology isn't necessarily what I'm interested in here, but it's where I want to make an impact. I'm interested in applying this - almost like a science of - trying to figure out how to build software tools that help tame the complexity of data structures into something useful. And ones that allow you to rapidly update data structures because they also change over time in bio. It's almost the exact opposite of, for me, my machine learning background because with big data, it's all about data scalability and mathematically computing industry to quickly handle huge volumes of data. This is like the exact opposite. And it's interesting in that sense of being a different sort of computer science frontier, trying to handle severe, messy data structures.

E: You mentioned starting as a bond trader. How did you first get into computer science?

N: In some ways, I feel like everything is different flavors of the same thing. The first time I ever programmed anything was copying and pasting a VBA script into Excel that would take excel cells then create a formula that would give you the Google maps travel time between two locations. At the time the Google maps API was free — it was probably in 2008 or something and I was in high school. It was amazing. I think for me, I spent so much time in Excel when I was young that I think in a very data-oriented way, which is not how many engineers think. I think many engineers tend to think in more data models and populating data in a less structured data model, whereas I worship databases and tables and structured data. What’s interesting is I feel like the arc of the computing industry has gone that way since then — now everyone loves all these analytical tools that are very data centric, like Databricks or Snowflake. There’s a lot of innovation happening.

I think now I’m pushing the paradigm on the industry because it’s a good paradigm to deal with complexity. That complexity means that it can’t be software engineers who are building these integrations, it has to be data scientists and practitioners. Our software engineers build a platform, and then the practitioners or our client engineers go in and actually write the business logic on top of it. I think that’s the common theme of what’s going on in the industry here.

E: I have just one more question for you here. It’s been an intense year for you starting your own company for the first time. What have been the biggest takeaways?

N: Especially in big B2B spaces like what we’re doing, ideas are very cheap. What matters is execution, which means you must have strong engineers and revenue, which means that you have to be able to find clients that are going to pay for your thing. It’s not necessarily even that hard — like you don’t have to sign contracts all the way, all the time to get it off the ground. You can show evidence of people being interested in things. People are willing to talk. I spent a good chunk of my time here at the start coding, of course, and working on the company itself. But I also spent a ton of time just networking, trying to find my way into these conversations, developing decks with the vision of my idea, and asking people, “Hey, is this what you would want? Is this interesting?”. That’s the way to get in, is finding the customers as early as possible, and finding out what their biggest problems are. It’s obvious, but you have to actually just go and find the customers and talk to them.