Another startup that replaced a spreadsheet

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Jason Hirshman is the founder of Uncountable, which helps R&D scientists with comprehensive data management system the whole team can work in.

That might sound like a complicated solution but in this interview you’ll find out that he got traction quickly just by taking common Silicon Valley data management into other verticals just outside of tech.

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Jason Hirshman

Jason Hirshman

Uncountable

Jason Hirshman is the founder of Uncountable, which helps R and D scientists move from isolated spreadsheets and projects to a comprehensive data management system the whole team can work in.

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Full Interview Transcript

Andrew: Hey there, freedom fighters come in to you directly from a couch in a random Airbnb in Austin, Texas, it’s, uh, Andrew Warner, you know, me, your old buddy or power for over a decade. The guy who’s been interviewing entrepreneurs about how they built their businesses.

And joining me is Jason Hershman, someone who basically left school early because he was so eager to get into entrepreneurship. And he created a company called uncounted. Which is a platform that’s built for R and D management, which frankly makes no sense to me when I just see that. But I understand, and you will too, why it’s a left so broad.

I invited him here to talk about why he left school, how big this business has gotten, how he got here by figuring out that there is a group of people whose business and lives still have not been touched enough by tech by software. And he said, why, why are they still. Spreadsheets, we can modernize them.

And so he did, and I invited him here to talk about how he did it and we can thanks to two phenomenal sponsors. The first Jason uses them. It’s a company that I’m going to be using to do my payroll. It’s called Gusto later on. I’ll tell you why you should go and try them for free at gusto.com/mixergy.

And the second I use also it’s for hosting websites. It’s called HostGator, but I’ll talk about both those later first. Jason. Good to have you here.

Jason: Oh, thanks for having me, Andrew. Really appreciate it.

Andrew: You’re all bootstrapped, right?

Jason: Yeah, we are, uh, we started back in 2016 and since then have been growing off of the revenue from our customers and sort of organic growth that we’re seeing from our user base and really excited about the potentials opportunities ahead of us.

Andrew: What is the revenue

Jason: We have around 5 million in annual revenue.

Andrew: well profitable?

Jason: Yeah. Um, pretty much since day one.

Uh, but the margin has changed certainly. And we keep expanding the team. Um, we look to expand the team, you know, 50 to a hundred percent year over year, and they’ve run a lot of great people that continue to push our product forward.

Andrew: I love the way you explained what you do before we got started. And I hope I didn’t now exhaust it, and that you’ll sound just as fresh as he did with me. But what is it that you do? And then we’ll go into a specific example.

Jason: Yeah. I mean, part of my job is trying to explain this world of R and D to engineers. Um, so I can do my best here. Um, but at the end of the day, what happens today in a research lab is you have scientists collecting data from experiments they’re running, and then generally putting that data in Excel. typically you’ll have the inputs for the experiment, what they’re changing in a separate sheet from the measurements.

So if you want to put them together, you do a bunch of manual data analysis to go and try to find trends and results. And the process here is really inefficient and we saw this and I’ve developed a tool that makes it really easy for all the data to go in one place. And also for you to collaborate with other sites.

We think of our tool as solving this problem of science. At scale, if you have a team of 200 researchers at an organization, you need to be doing things differently than if you have a single person at a lab venture that a computer, you want to make sure that all of the scientists coming into your organization can get access to the results that you’ve been running over the last few years and want to make sure that teams globally can all learn from each other as do.

As that’s the problem that we’re solving along with a lot of other lab management tasks, like managing inventory, um, creating lab requests, um, and managing, you know, different tasks that happen in the lab and making sure that all the data is centralized for the Lab

Andrew: Lab requests, meaning like the way that most business people might ask for an office space or a conference room, and they need to use software probably by Google or someone, your clients are using yours.

Jason: Well, the, the lab request here would be, you have an analytical team that’s in charge of running these really expensive experiments. And when a scientist wants their sample to be tested, they’ll actually issue a request. The analytical team saying I have the sample. I want it to undergo the following three types of. Um, and I, if you know, it’s a high priority request for a customer, let’s go get, get this done. And this analytical team is receiving a bunch of these different requests and needs to send data back to the scientists. And, but before in Caldwell, a lot of these companies were actually sending PDFs with results from those experiments.

So if a scientist wants to go and analyze that. Parsing that PDF or copy and pasting tables

Andrew: literally.

Jason: of manipulating it around. Yeah. It’s a, it’s actually more common than you’d think. And, um, it’s certainly makes for a pretty inefficient process where no, one’s truly happy because the analytical team doesn’t like that they’re receiving data from some sort of custom form environment and the scientist doesn’t like that they’re having to do this translation work in between.

Um, so just the communication overhead in a big lab is something that just software hasn’t gone.

Andrew: How the hell did you see that this was a problem?

Jason: Yeah. So when we were starting accountable, we were looking for ways of taking data management techniques from Silicon valley and moving them outside of just tech and finance to other industries. And what we quickly realized was that there are a lot of places where data management is necessary and helpful, but not a lot of places where you can sort of change a business quickly by influencing decision.

If you go to a manufacturing firm and tell them to change the way they do manufacturing, they’re going to tell you it’s super expensive and they can’t really run a trial. Uh, but it turns out that R and D is an area that iterates really quickly. If you tell an RD scientist, um, to maybe try different way of running an experiment, they can go do that because it’s their job to experiment with.

And so that fast iteration time allows them to react to recommendations based on their data more quickly, and allows us to have higher leverage and proposing innovative solutions and different ways of organizing.

Andrew: But how did you figure this out? You weren’t in that space? What were you studying in school at the time?

Jason: Yeah. So I come from a math and computer science background. Um, I’ve always loved software engineering and have worked with startups before. Um, but I also love solving problems with math. So I studied loss statistics and understand how data can have a really big impact on the way people make decisions.

Did not have an army background. Uh, but when we started in Cabo, we started talking with a lot of different companies and it was just having a lot of conversations. We were working out of our apartment then Sunnyvale and trying to figure out kind of what it meant to start a company that really tried to impact an industry we were familiar with.

And our biggest weakness at that point was lack of domain expertise. But I like to think that we essentially spent a year gaining. We talked with So many people that we started to really understand this problem. Well, and once we had uncovered that we could have this leverage in the R D environment, and we went and validated that with a lot of different organizations, we were pretty confident that this was the problem that we were going to go hire a team to solve and build this.

Andrew: So my, my hunches that you said where at Stanford, right? That’s where you were at the time.

Jason: I was, um, my co-founder is actually at a, another startup and then brought a third co-founder, um, who had a study with him.

Andrew: Okay, but you’re bringing in all this background and saying where clearly a smart group of people we want to see if there’s anything we can help you with. And that’s what opened the door, multiple industries. And then eventually R and D is where you zeroed in. Am I understanding right?

Jason: Yeah. Um, you know, the, the reality was there was a lot of hype. There still is. Artificial intelligence, the cloud data, all the buzzwords that you hear and that we use the hype to get in the door, frankly. Um, and what we found was that that was a good and bad strategy. It was good because you got in the door, it was bad because the hype creates misaligned expectations

Andrew: Give me an example who where’s, where’s an industry or a company that you got in the door because of this. And then you, you realize this is just ridiculous because we’re promising or they’re believing that we’re going to do more than we ever could.

Jason: Well, a lot of it is less about belief and more about misunderstanding of what’s possible or what can change them for night. Um, so one of the first companies we worked with was Cooper-Standard and we actually want. Uh, pace innovation award or a nomination price, innovation work with them, uh, for kind of some of the data work that we were doing.

And we were able to show that by leveraging the scientific data and structuring and organizing it, we could.

actually speed up their R and D um, development exercises. Now, the problem was is that if you simply take it one project at a time, You can’t get better. Um, and so the, the analogy here and sort of the machine learning world is if you stick with the same training set or the same size of training set your, your model doesn’t really improve without a very big technological advance.

And so we set up. Well, if we want to help, Cooper-Standard more and we want to make a bigger impact than other companies. We started working with companies like Beyersdorf surf that do a lot of consumer products, really innovative companies like carbon 3d that do 3d printing things. Um, we want to make sure that we can build larger and larger data sets for them.

And so we transitioned from this model of saying, we’re going to help you project by project to, we want to help your whole organization manage data better instead of. Leaning into sort of hype around the cloud and AI, we said, look, there’s problems on the ground today with how you’re communicating, let us help you make your scientists better at what they do and give them the tools.

They need to be able to lean into those technologies. And so our platform today, um, expose the scientists to a lot of advanced analytics capabilities. Um, there wouldn’t be possible without the data all in one place, or at least would be a ton of work to try and like.

Andrew: Just so I understand how you ended up with this with R and D. Can you give me an example of another place and other type of customer that you were aiming at before this? And you realize, okay. Our, in our intelligence, our abilities are not gonna apply here.

Jason: Yeah. Um, so our CEO Knoll, um, I met him working at a second spectrum, which is a sports analytics company. Um, and there are, um, we basically realized that that the secret sauce, the company was being able to manipulate and understand how to analyze data on a basketball. Which was really this sort of motion data.

And the name on carnival comes from this idea of being able to analyze continuous data, that there’s something sort of different about the continuity that comes from emotion series that you don’t get from a spreadsheet of information. And so we took that, um, and broadened will our third co-founder. To go in and try to investigate how that type of data could be relevant to the manufacturing world and to other industries.

And basically found that the continuous aspect was certainly a powerful place to try to build a business, but we didn’t have the hardware expertise necessary to build like internet of things, company. Um, and so the R and D space was much more natural fit to actually apply similar optimization techniques and build the kind of core software.

Um, that we would be a better fit for, for trying to solve. And so far, what we’ve seen is that this problem is a lot bigger than we thought it was. It applies to a lot more industries. It applies to a lot of different types of R and D environments. And we’re really excited about seeing how fast this can really expand and how many use cases we can cover.

Andrew: You worked for Palentier before, right?

Jason: I did an internship there. I was at Stanford.

Andrew: What did you learn by being there?

Jason: Um, well, enterprise data management is certainly What I learned. And I think that when I look at some of the problems that we’re facing today, and some of the problems that lbs here faced and making a transition from a highly customized government products, um, to sort of more of their industry offering, um, there’s a lot of overlap in some of those challenges and what I think, um, one of the challenges that Palintir face was trying to build a super generic That could fit and mold itself to a lot of different industry problems. And,

Andrew: product that they, they were trying to create.

Jason: uh, they’re basically trying to create. counter does, is they, um, analyze data as like a series of graph patterns. They’re looking at connections between data that would be typically stored in SQL database, which is somewhat inaccessible to someone who doesn’t have a lot of database understanding of knowledge.

And they try to play that solution generically to a lot of different industries. And the thing we do definitely with uncountable is really be very specific to this R and D. Is, you know, explicitly we’re building a product for scientists. Um, so if you have a PhD in chemistry working in industry, we want to solve your problems.

And we’re not trying to expand this to every sort of business problem within these types of companies. We want to be very focused on these kinds of highly tactical areas where this RD data is super critical. And the reason why this is high value is because the innovation of these companies is highly.

On the success of their R and D environment. This is how they continue to get ahead and make sure that they can effectively develop the future.

Andrew: Okay. You were telling me about your first customer. And I think there’s a good example there of what you do. Tell me how you, how you landed them as a customer. And then let’s talk about an example of what you do with them.

Jason: Yeah. So we actually reached out to one of their executives. Um, uh, who’s very, forward-thinking. And very friendly to startups like us just getting off the ground. Um, and I think my advice for other entrepreneurs out there is, um, it’s not necessarily about the companies you reach out to it’s about the people there And how willing they are to take a chance on you.

Um, and we’ve seen that a couple of times and the companies that we’ve acquired. And what he told us as essentially that there’s a lot of R and D experiments being run. Um, it’s not clear why scientists are choosing certain ones. There’s not communication happening between their lab. That’s located in Indiana and the lab that’s located in Normandy and France and everything is sort of happening independently.

And so you would

Andrew: And so you’re saying people in labs, scientists and labs would just say, I have an idea for something I should be studying. Let’s do that.

Jason: It’s a little bit more focused and industry development. So typically, um, they, they’re a tier one automotive supplier Cooper standard, um, and they make things like rubber hoses for engines. And so what will happen is a car manufacturer will go to them and say, developing a new car. I would like this pose to have this certain property, the certain density, the certain strength go and develop a product for me.

And they’ll send out these bids to a bunch of different companies for this. And it’s the goal of the R and D team to go and match all those specifications. But typically the same car manufactured in America will have slightly different requirements from the cars manufactured in Europe, even from the same automotive company.

Um, and that’s because of environmental standards or other regulations. And so you’d have European team working in the European standards and the U S team working on the American standards, but fundamentally developing the same product for the same make of car. And, and ultimately you want them communicating so that they can transfer expertise.

Which raw materials are working, which properties are hard to achieve, um, and, and really make sure that they’re developing in concert. And we see this problem a lot across a lot of different, um, customers we work with where, especially in a globalized world, um, you want communication that’s happening at sort of the numeric and scientific level.

You know, we literally, if you have someone speaking French and someone’s speaking English, They should still be able to look at each other’s data because ultimately these are numbers and metric measurements. Um, but that simply wasn’t happening because all emails happening are all communication happening over email and PDFs, where there was more of a translation.

Andrew: Yeah. Okay. And so they were sometimes doing the same experiment, maybe getting results that would help the other team, but not passing it in a way that would benefit the other team. You, you go in and say, I think we can solve this. And your first solution for it was.

Jason: Yeah. So a lot of it is latency, right? Is it may be they, they communicate, but it takes a week for the results to go back and forth. And in our platform, you’re entering data. It’s all in the cloud. You can just log in and see instantly someone else’s results. Um,

and our solution here is that is to build a software platform where when I enter or result in everyone else at my company, Uh, can see that result if they’re working on a similar project.

And so it’s really creating sort of a whole file system for R and D in the cloud where you can store data in a very structured manner. And the other thing that’s different about our system from a lot of legacy software. Is that we focus on sort of those data structure as opposed to sort of a notebook or a historical log of what’s happening in the lab.

So that the typical picture of a scientist a lab is they’re jotting down notes in a notebook next to them and recording chronologically what’s happening. And the problem with that is that if I want to try to find an insight that they had, I have to basically read through their history. There’s no structured search of that notebook.

Um, and we try to solve that problem as well. Focusing on each individual experiment as essentially an animal of work instead of a page in a lab notebook.

Andrew: I said good analogy. Okay. All right. Let me talk about my first sponsor and then we’ll continue on with the story. First sponsor is Gusto. You use Gusto, right?

Jason: Yeah. We use Gusto to manage payroll, um, and kind of as our HR plus.

Andrew: When you say HR platform, how do you mean, what else does it do?

Jason: Uh, we provide, uh, 4, 9, 8 for our employees through it. Uh, we also, um, use their managed health care benefits, and try and make it as easy as possible for employees to understand what’s happening, um, and make it easy on us to, to try to manage this.

Andrew: Was this a decision you made personally or as the company was the company big enough when you decided that someone else made that decision to use Gusto?

Jason: Um, I don’t know if I made it personally, uh, will is, uh, in my co-founders in charge of a lot of that. Uh, but certainly we knew that as soon as we hired our first employee, uh, we wanted to present ourselves as a mature organization that took care of them. And it was important to us that we give healthcare right away and have a real payroll.

Andrew: Even with the first employee. Wow. All right. For anyone out there who is now thinking, should I switch or should I not? I think the beginning of the year is a great time to make a switch so that everything starts off fresh, clean, and is all organized together. That’s why I’m switching 20, 22. I’m going to be on Gusto to pay.

And by the way they do full-time employees, but also contractors and they make it easy for you to pay them. And for them to know what they’re getting paid and for them to access all the data they need, it’s just beautifully done. And I could talk your ear off. But I think if you just experience it for yourself, you’ll know if it fits for you or not.

And so I’m gonna let you use it for free right now. All you have to do is go to gusto.com/mixergy that’s G U S T o.com/m I X E R G Y. All right. I’m grateful to them for sponsoring. Um, how did you get your next customer?

Jason: Um, well, once we got the first one, uh, we knew what to look for. We knew that the person experiencing this pain is the person that’s trying to manage a bunch of RD projects. So the person who’s wondering, why is R and D taking so long? Why can’t I find that next great thing. Um, and so we started reaching out to companies with that.

And we started targeting companies that were sort of the right size for us, that meant, um, you know, multiple billions in revenue, essentially. And making sure that we got to a point where, uh, we could get that scale from these users and understand that this wasn’t just one person’s pet problem, but really an organizational issue, uh, for the companies we would battle.

Andrew: How, how did you find the right person at the right company? Finding the right company, I guess is pretty easy, right? We’re talking about big businesses that aren’t hiding. How’d you find the right person there and how’d you connect to them.

Jason: Uh, well, you find a lot of the wrong people. First, I think is the, it’s the right answer. Here is you got, get, get, get in the door And talk with the wrong people for you find the right one. Um, and it’s basically knowing that you might not be talking to the right person. How do I find the person who’s both experiencing this problem and in a position where they could bring us in to help solve it.

And as we’ve grown as a company, that person. Been higher and higher up in the food chain of these organizations. And at this point, when we sell to an organization, we’re typically talking about a multi-year rollout of the software, across many different teams, which requires some sort of executive approval.

Uh, but early on, it was usually an R and D director. Um, maybe even just a manager and sometimes the vice-president.

Andrew: And they would just take your call randomly, or was it enough to say I worked with this other business? I think you might benefit

Jason: Uh, well, we sent personal emails, so we never used like a mass outreach system and it could be because of that. It could just because our message was great, but we actually got a fairly good reply

Andrew: personal cold emails.

Jason: Yeah. A lot of personal cold emails.

Andrew: Wow. I’m guessing it’s because they don’t get a lot of spam and cold emails that are like, that apply to them. I get a ton of them because people want to be on the podcast or they want a sponsor. I’m guessing that someone who’s heading an R and D department doesn’t get as much. Am I right?

Jason: There definitely is not the software sales infrastructure in the R and D role that there aren’t a lot of places. If you’re a marketing, exac you’re probably getting a lot more software reach out than you are if you’re R and D exec. And I think that does make a huge difference. Um, uh, but certainly we, we saw.

Um, as a good option to just keep reaching out. And, uh, we also got a lot of our customers through word of mouth and we’ve found connections from companies we work with. And it’s been really great seeing how collaborative the R and D world is. As soon as you get away from the direct competitors, um, are these very secretive if you have two companies competing over a deal, um, but they really like to learn from each other, um, when you’re talking across.

Andrew: Will they be so competitive that they won’t let you work with a competitor of theirs, even though you’re only providing software.

Jason: Um, so our goal is to make them feel as comfortable as possible with a data have. And what would we tell the companies, as you know, we are data engineers, we’re software.

engineers. Um, one of our first priorities is security and making sure that the data is isolated insecure, and we want you to know that you’re doing.

Um, as safe with us as it is in your own servers. And, uh, we do that through a lot of ways, including getting, um, security audits, even when we’re working from our apartment, we actually got our first security on it. Um, and have had professional SOC two, two auditors for many years. Um, and that enables us to, to go and say, look, you know, we’re gonna trust you with our D your data.

We’re not gonna share it. We’re not gonna cross insights, uh, within these industries and make sure that, um, everything that’s yours stays yours.

Andrew: This is the thing you’ve wanted to do for a long time. From what I understand as a kid, you loved programming. You love trying different languages in high school, you worked for a startup. What was it? Bench, press, bench prep.

Jason: Yeah.

Andrew: What’s that?

Jason: Uh, well, Ben propose, uh, there is a test preparation company that, um, helps to essentially digitalize, um, test prep books for things like the sat or the M cat, um, various different tests. And, um, I, I learned, I was fortunate enough to be able to learn from a lot of engineers, um, and get a lot of exposure to different technologies and the way their make a product.

And it can be really intimidating to think about. Making a company or making a full product. Uh, but I got experiences where, you know, I was building web servers, um, and doing things basically from scratch, which allowed me to see that some of the things that?

seem scary or not so scary once you spend a little bit of time with it.

Um, And then coming into school, I spent a lot of time. Um, at places like Starbucks, actually working with companies, they’re being a part of their team, which is kind of Stanford startup accelerator, and making sure that I got exposed to a lot of the different problems that startups were facing and founders face as they try to grow their.

Andrew: And what seems to have gotten you really fired up was you were watching around 2008, the iPhone allowed apps, apps were being made, being shipped. People were excited about software again. And you said, I want to be a part of that. Am I right?

Jason: Yeah. I grew up in the Chicago area. Um, and I remember watching keynotes, not only from, from apple, but also Google and all the tech companies, um, it back. You know, 2008 era and thinking I want it to be in San Francisco and I wanted to be part of that. community. Um, and so when I got the lucky enough to go out to school out here, um, and I never looked back and now still living.

Andrew: I remember that. I remember the days when Steve jobs would do his presentations. If I had an interview scheduled a hundred percent, it was going to get canceled before the, uh, before the presentation, because people wanted to be there for the presentation. I would have to go. And I also wanted to watch it remotely or read those live blogs and I would have to schedule some kind of important phone call that I, and the other person couldn’t get out of just so I wouldn’t waste my time.

Just refreshing, refreshing, refreshing to see what he announced. It seems like you had similar experiences.

Jason: I think there is a lot of excitement around what the web could enable, um, and more generally other software and hardware that was coming out at that time, um, that there really was a turning point for kind of interest in, um, the Silicon valley and the, the types of companies you could make essentially on your own from hope.

Um, and in no longer required a lot of sort of capital to go and. You know, rent a server essentially, and deploy a website and seeing that and seeing the next generation of companies, um, essentially start to be founded, um, post 2008, uh, really made me excited about the type of impact you could have as an engineer.

Um, and certainly, uh, that was a big part of the reason why I’m still here today. And.

Andrew: This the time that you started though was 2016. And I feel like a lot of the excitement had gone away by then apps were already more mainstream and people were starting to think, I don’t want another app. Web two. Oh. Was kind of a dead experience. It was more like everything is now consolidating around a handful of companies.

Right. Were you feeling.

Jason: Um,

I was not personally feeling it. It still felt exciting to me. Um, but, but I think the, there was a lot more institutionalization of a lot of the concepts for how to start a company And uh, That I think benefited us more than anything, you know, from the start, our thesis here was we’re going to bring certain types of technology to industries that just don’t take advantage of it today.

Um, so to the extent that maybe certain areas will we’re well-trodden and starting a new app was harder and more saturated. Certainly enterprise software is not that way. Um, and from day one, we were trying to be in the press off.

Andrew: And you’re saying the clients that you are pursuing weren’t in the cloud. For example, weren’t super into new tech. That’s the way you are experiencing.

Jason: Well being in the cloud, certainly, uh, there are certainly cases where we talk with a company in 2017 who said no way, we’re never adopting a cloud application. And today they come to us and say, You know what we, we trust you, we’re going to find the data with you in the cloud. And, um, there’ve been complete one eighties over a course of a couple of years.

And one of the things that doing, um, startup has taught me and really be engaged with in Kabul for five years now, it’s taught me is that, um, things do change and, um, you really have to take a long-term perspective and make sure that you’re pushing your company in the direction of where the trends are headed.

And, and certainly we made a bet that companies. Uh, doc cloud software. And that you would think that, that, that would already be proven right? Five years ago. Uh, but it’s still something that’s kind of consolidate.

Andrew: And you were making these phone calls. Does customer discovery calls from your dorm room at Stanford, trying to understand what was going on, what they needed, if it wasn’t for this, if you hadn’t landed on this business, what’s another one that you might’ve jumped in.

Jason: Um, well, we certainly explored a lot of different areas. Um, we looked at kind of production planning for, for factories. Um, we worked with, um, Music festivals and it’s kind of some of their data. Um, there was a lot of different things that we tried and to us, You know it was really about the impact that we can make.

Uh, what got us excited about this R and D problem is that they really did seem like something where you could take data, formulate a decision or a recommendation, and actually have leverage on the business. And that wasn’t true if some of the other application areas that we have looked at, um, and that made a big difference to our.

Eventual decision to really go all in on this.

Andrew: You know what Jason, I see that a lot of it is about data. You know, that if you could make sense of data, if you can organize it and make it more accessible. Someone’s going to benefit from it. I wonder if you would have thought about say all the health data that consumers are collecting right now, both from lab results that they’re getting from doctors that don’t make much sense to us.

And the doctor just says you’re healthy or you’re not. And from the watches and the scales and everything else is more and more connected. Would that have been a business that you would have, that you would have considered making sense of or did it need to be a business utility so that you can have real financial impact and.

You know, build a business. That’s bootstrapped.

Jason: Well, I think more than a financial impact, we wanted to have an impact on the way the supply chains work. So as a company is to accelerate R and D by a factor of 10. And we want that product that’s coming out in a year to come out in a month instead, and really speed up the whole development process.

Because what we see from companies is that they don’t really make advances until the current. Higher up in the supply chain can feed them new materials, but then they want to be able to take advantage of that as quickly as possible. And we want to enable that, um, in terms of working with kind of consumer health data, uh, there are certainly a lot of regulatory barriers, um, and coordination barriers.

Uh, they make a really hard, uh, Build a company there, or kind of take advantage of All that information. Um, you know, at one principal and starting a company is to think about, well, when you become big enough, who are your competitors and when you think about consumer health, um, certainly you start to come up with names like apple and Google faster than what you, um, are starting an enterprise R D company.

And so. Um, that, and I think that’s a big part of it as well, is that there are already companies that are really well-suited to try to tackle that.

Andrew: All right. Second sponsor HostGator. I use them to host my website. If you need a website, hosted, do what I did go to hostgator.com/mixergy. You’ll get a site that works it’s inexpensive and it’ll grow with you. They do such a great job for me. hostgator.com/mixergy. That URL will make their already low price, even though.

All right. I’m grateful to them for sponsoring w Ari, our producer asked you, tell us, tell her about the lowest point in your business. And this was surprising. It was hiring your first engineer. You’re super smart. You’re in an environment where there are a bunch of people who are smart people who are looking to handle challenging problems that are different.

That once someone was not a snake oil salesman or potentially one, you definitely aren’t that right. You’re much more of an engineer like them. Then most companies here, what was this?

Jason: Yeah. So the struggle is with finding an engineer, we really excited about, um, you know, We all three of us has founders have engineering backgrounds. Um, and we want to add people to the team that we’re excited about. Um, the passive very high technical bar. Um, and then, uh, it can also be future leaders and we went and searched and it took a very long time to find a candidate that they’re excited about bringing you on.

And during that whole process, it’s constantly felt like a battle, not to try to lower expectations and say, you know what? We can’t find the person we’re looking for. We’re just going to lower our bar or we’re going to give up on certain features is the first trying to find. And that was really, really challenging.

And we had already hired some great data scientists. We had a great salesperson. It was very challenging to go in and find the engineering hire. And I think part of it was, I had never done it before. I had never, um, hired someone. I convinced them, look, you know, you’re going to come in and make a product.

Is injustice very early stages today. And you’re going to be a key part of that. Um, and for some of the early data science hires I made, I had been doing the role, um, for a while and knew exactly on a day-to-day basis, what they were going to be doing. I’d written like a handbook for them to come into. Um, and for engineering, you know, we wanted someone who could come in and really.

Take ownership over a really big part of the product and didn’t want to have to lower our bar. And so, uh, it was a very long search before we found, um, reverse engineer will have who came in and has really been awesome for the team. And, um, he’s actually helped us to continue to grow the team and make sure that we never have to lower that bar.

And since then we’ve hired a lot of great engineers. And I can’t say that it’s a lot easier to. Uh, but certainly my expectations have changed around what hiring engineers in Silicon valley means and how to do it effectively.

Andrew: What have you, what have you learned? And this is by the way we’ll Goldie.

Jason: Yeah.

We’ll go there with our first, um, software engineering hire. Um, and we, we we’ve learned that it’s hard. I think the day we’ve learned that, um, engineers have a lot of great options and the hiring market is very inefficient. Um, there are a lot of engineers that. Don’t explore all the options that are out there.

And so just getting over the initial hump of getting in touch with them and having them consider your company is really, really challenging. Um, at one point I talked with a bunch of. Um, really talented engineers and basically ask them, how’d you find your current job? Uh, one of them told me that they switched jobs recently because they saw a billboard on the highway or like what company is this?

And they interviewed at one company, got the job and took it. And I’m going to we’re off the John Margaret for the next two years. And certainly as a bootstrap company, we’re not buying San Francisco billboards. Um, and so we needed to find other solutions and really hit hard. Um, a lot of different areas to try to define the best candidates.

And one of those has been trying to recruit the best students coming out of college. Uh, when you’re in school, uh, you, you started naturally happens like two month period where you’re looking for a job at some point, which is actually much longer than when you’re in industry and only consider one or two companies.

Um, but we, we also, um, are trying to find more senior candidates through a lot of other sources as well.

Andrew: What’s worked well for you

Jason: Um,

Andrew: the other sources,

Jason: I similar to help me find business, which is a lot of coal, free chance. A lot of trying to out to engineers, tell us, tell them what we’re doing. Tell them about the opportunity we have in our engineering philosophy.

Um, I think one of the things that’s really important to us as a company is being very engineering focused. Um, we truly believe that the best product in the space is going to weigh it out and. We think that our biggest advantage is finding awesome engineers that can come in and really transform the product.

And so there’s a big emphasis on, uh, career growth and personal growth of engineers. I’m giving engineers more responsibility that they, than they can handle right away. Um, even for someone coming right out of school, I put, especially for someone who’s been in industry for awhile, um, and really allowing them to make a lot of, um, creative and executive decisions.

And, uh, with that, um, once we get engineers in the door, we can really?

show them why this might be a good fit for them. Uh, but it’s the cold for each house that at least get the first message.

Andrew: What do you do to get them to pay attention to you when you just reaching out to them with cold messages?

Jason: Um, well, I think people don’t really know about this industry. So the first thing is actually explained to them what we’re doing and why. Um, and I think a lot of engineers, uh, at one point were interested in science, um, whether they pursue that in school or just like, remember their high school chemistry class.

Andrew: Yeah.

Jason: of excited about this problem area and it is a little bit different. Uh, we are end users of this product are typically PhD scientists. Um, there are people that are really passionate about their data, about the products I’m working on. I’ve learned a ton of science across material science, chemistry, biology, Um,

just from trying to build a product for them.

And I think it’s a really interesting domain that kind of captures the attention of engineers. Once they get a message in there.

Andrew: And how do you find the right people to pursue?

Jason: Um, a lot, a lot of LinkedIn. Um, I think the, the key thing for us Is finding people that, um, either might be overlooked by other companies or may not consider it.

a career change for awhile, um, and kind of reaching out to them and saying, look, this opportunity is available. Here are the reasons why it might make sense for you.

And, um, seeing if we can get those initial conversations. Um, but it’s hard and, you know, I did not have good expectations going into trying to hire engineers in such a competitive hiring environment. And we just opened an office in New York. So it gives us kind of access to both coasts. And there’s certainly a lot of engineers right now who miss being in an office so we can offer that.

Um, but even in both New York and San Francisco, they’re both very hard places to find engineers.

Andrew: All right. The website is uncountable.com. And are you now, are you doing these interviews because you are thinking about raising money, is that. Partially why you’re going out or is it for hiring?

Jason: Um, yeah, it’s not for raising, um, it’s for kind of getting your name out there, Uh, partially for hiring partially for sort of internal brand and Silicon valley and the tech world. Um, most of our marketing materials end up in, you know, R and D magazines that engineers are not reading. Um, and so we want to start to build more of a presence and.

Because we are bootstrapped. We don’t have a VC network to leverage to get easy intros to media companies. And it’s one of those asymmetric advantages that don’t matter to our customers, but do matter to you to potential hires that you want to try to get.

Andrew: Yeah. Yeah. In the beginning, when I was trying to get guests to do interviews, I didn’t have an audience to promise them, but I could say, you know, when someone’s looking to work for you on their drive over, they’re going to listen to this interview to get a sense of whether you’re the right fit. When they hear from you, they don’t know who you are.

They’re going to Google you. And they’re going to come up with this and they’re going to decide whether they like you enough to work with you or not. So that was a big, uh, conversion point for me. All right. Well, Jason, thanks so much for being on here.

Jason: No, thank you so much for having me. I definitely encourage people to discover a website and kind of a lot com we have a lot of materials about our hiring process interviews, et cetera, because we know that hiring can be very opaque from a perspective of the candidate. Trying to make it more transparent and talk about what we do and why.

Um, and certainly are excited to answer questions. So if anyone wants to email me, um, emails to jason@accountable.com, um, I try to firstly, respond to All engineers to reach out. So feel free to do that.

Andrew: All right. And thank you to the two sponsors, hostgator.com/mixergy and gusto.com/mixergy. Thanks, bye. One,

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