Subscribe
Share
Share
Embed
Honest conversations with the engineering leaders, CTOs, founders, and engineers building real software with real teams. No fluff, no hype — just the messy, human side of getting great products out the door.
Zhenya Rozinskiy - Mirigos (00:06)
All right, thank you so much for joining us. We're recording this edition of a podcast. We call this the Business of Software, where we talk about everything that has to do with getting the apps, getting the technology out there. We don't really focus on the engineering portion that's not as interesting or not as interesting for our audience. We talk about the business side of things. And then of course we talk about...
know, technology and how it affects our lives and the latest and greatest things we're seeing and how it literally affects everything we do. My name is Zhenya. I run a company, I'm a CEO and the founder of a company called Mirigos. We are a service company actually, where we help clients hire people, get the really good top notch clients or developers I should say, from Latin America and Eastern Europe.
We're not outsourcing shop. We've actually, our tagline is we fixed what's broken in outsourcing. So there's lots of interesting topics we can cover in that, but that's not the point today. Tell us about yourself.
Isaac (01:06)
Yeah, for sure. So my name's Isaac. I manage an analytics engineering data engineering team at a Cambridge-based fintech startup called Insurify. We are an online platform for car insurance comparison. Think like Expedia, Google Flights, Kayak, both for car insurance. My job is to make sure the organization has really accurate prompt.
and actual information for decision making. We are deploying machine learning at scale, and my job is to make sure that garbage in, garbage out. I don't want there to be any garbage or as little as possible.
Zhenya Rozinskiy - Mirigos (01:34)
Yep.
Very interesting. So you're actually touching on both things of our conversation, right? Technology and the product that you guys are doing and how you're doing and we'll talk about that. So AI, and I hate, I don't wanna turn this podcast about AI. It becomes that way and you're a perfect candidate for it, right? Because my question is,
Isaac (01:53)
It always does become that way.
Zhenya Rozinskiy - Mirigos (02:00)
I use AI, I use AI to help my life. I've actually built very, you know, pretty elaborate prompts, sort of custom agents that help me in my daily things. You know, I have an agent, and again, I'm talking about a very complicated prompting to help with my company marketing. But I also have one for my day in, day out, you know, daily business side of life, right? Not my family life, but my...
Do I wanna refinance the home? Do I wanna, which? Car insurance is better and the, it's very...
Isaac (02:31)
There's
a lot of stuff to process every day. There's far too much information. Everyone's of DDoSed mentally.
Zhenya Rozinskiy - Mirigos (02:36)
Exactly. And so one thing that I was working with over the last several weeks is insurance. It wasn't specifically car insurance. It was umbrella insurance, but it matter. was insurance. And I was using AI to compare different policies and compare different things and tell me which one's better. What's that?
Isaac (02:50)
You should check us out,
Check us out. What's up? Don't use an AI, use our product.
Zhenya Rozinskiy - Mirigos (02:55)
So do tell, right? I wanna hear more about what do you guys do and how you do this. And car insurance is what everybody has, right? It's part of life. We all have cars. We all have car insurance. And historically, it's been one of those things. I've been with this insurance for so long and it's probably fine. And nobody goes around looking for a new one every six months or a year. And we end up paying more or not having good coverage.
So tell us about just a little bit, I'm just fascinated, right? This is just fascinating.
Isaac (03:24)
Yeah, so I mean, I can't, I don't want to, I don't want to reveal like all of our, all of our secrets, but you know, I think with, with so many things, whether it's travel or hotels or car insurance, you know, people want accurate information to make smart decisions about their financial life. Insurance is a really important product, financial product for millions of people in America. It's required for Americans.
Zhenya Rozinskiy - Mirigos (03:29)
No, of course.
Isaac (03:52)
So making sure that people are getting a good rate, saving money is kind the name of the game. think there is a highly liquid online marketplace for car insurance, and we're kind of playing in that space. Making sure you can find the right customers and give them the right options so that they can save some money and you can make some money at the same time.
Zhenya Rozinskiy - Mirigos (04:12)
So are you more of a lead generator for insurance companies?
Isaac (04:16)
Yeah, I mean, think it's sort of... I would say no. mean, we have like quoting on the site. We have quoting on the site. We're trying to make sure people get the most options. So, but I think that like, know, all of these things, you know, we play a little bit in the tech world. We're trying to get more into bundling, but yeah.
Zhenya Rozinskiy - Mirigos (04:33)
Cool, I'll definitely check it out. This is like, it's just cool. Let's talk a bit about people and you, a lot of it is data, a lot of it is engineering. We live in a world that's changing day in, day out with again, technology development and just the progression is enormous.
Isaac (04:42)
Mm. Mm.
It's no, it's, I
mean, it's, it's, it's, kind of terrifying, honestly. I mean, it's like, you know, I'm, I'm, I'm, I'm very early in my career. I've literally had one job. I joined in sure if I straight out of college, I've had a great experience here, been able to get a lot of opportunity here. But yeah, I mean, like, you know, I have friends who looking for jobs.
I think when it comes to AI and when it comes to being a young person working today, it's hard to know what to The only thing that's certain is uncertainty, especially when it comes to software development, intellectual labor. I think that one thing that I like a lot about this job, I think is hopefully insulating me at least a little bit, is think a lot of this is managing systems.
Zhenya Rozinskiy - Mirigos (05:17)
Mm-hmm.
Isaac (05:35)
I think that
going to become a bigger thing, and I think it's going to be a bigger thing for a total smaller number relative to the job losses, is going to be managing systems at scale, AI agents taking action, AI systems and machine learning systems. And I think actually the line is going to get increasingly blurred between what is traditional ML and what is AI. But managing those systems and making sure that those systems
are making the right business decisions to enable whatever your objectives are.
Zhenya Rozinskiy - Mirigos (06:03)
So when it comes to hiring, I had a conversation earlier today, an hour ago in fact, we talked, it was a hiring manager, I wasn't recording podcasts, I was talking to a client and we were talking about their hiring and stuff. And what he mentioned that they noticed that a lot of people, a lot of engineers have been around for a long time, they're very hesitant, they're very resistant to change, they're very resistant to this whole machine learning, AI processing.
Isaac (06:24)
I mean, of
course there was a sense of change. mean, like, if you've been a software engineer at Google for 20 years making stupid money and like, it is kind of insane that like humans, our thing is that we have brains. Like that's our competitive advantage versus all the other species. And now like our competitive advantage is being eroded. Like of course you blow resistance to change. It's like really, it's totally reasonable, especially when you like effectively won the last 20 years.
Zhenya Rozinskiy - Mirigos (06:45)
Yep.
Right.
Isaac (06:51)
Like, yeah,
and I think, so yeah, I don't know. don't know. I think people are really reasonable. I think people are right to be afraid, basically.
Zhenya Rozinskiy - Mirigos (06:59)
So how do you approach that from a hiring point of view? When you guys hire, what happens? You're obviously right, you're young, mentioned you take your first job out of college. So for you, it's not even a change, it's nature. This is part of your life from day one. But somebody who's been in the industry for 30, 40 years, ooh.
Isaac (07:15)
Yeah, I so I run hiring internally for analysts, and we mostly hire analysts as like new grads out of college. I think what I think about when it comes to hiring analysts especially is finding someone with the right fundamentals. It's about hiring for ceiling, it's about hiring for slope. It's not about hiring someone where they are, it's about hiring for someone where they can be. And I think what it comes, like what it comes down to when it comes to those factors,
Are underlying traits do you have drive? You're not gonna make someone work harder than they're gonna work They're gonna work exactly as hard as they feel like working and like God bless them If you want to like if you don't want to work that hard, you're not that ambitious. That's fine You shouldn't have to be but you want to find people who are who have that drive who are gonna add I think there was a podcast like Lenny's podcast there. I forget who was talking about Being exothermic adding energy to the system. You want to find people who are gonna bring that energy
Next, you want to people who are just like really smart. Like I'm sorry, but like you want people who are really smart, like especially in an analytical world and the engineering world, like the raw horsepower of your brain matters a lot, especially when you got like AI nipping. And then I think the last thing is just like being a good person. Like I want to work with people who are nice, who I'm going to want to get a beer with after work. I think, I mean, like honestly,
Really what I believe is that the liberal arts education is so important for this. Like this is one of the things that like, there's so much discourse about, we should all go into the trades. We should all be plumbers. And you know, it's awesome. Like, cool. If you want to do that, like that's great for you. But like a liberal arts education, which actually creates someone who has incredibly good reading skills, critical thinking skills, and the math skills has like that, you know, that constellation of traits.
Zhenya Rozinskiy - Mirigos (08:39)
I agree.
Isaac (09:02)
So that you can be flexible, so you can continue to learn is gonna mean that like, whatever happens, whatever happens, you're gonna be that.
Zhenya Rozinskiy - Mirigos (09:09)
So you've touched on something that very much resonates with my thinking. I've been in a hiring manager role for over 25 years and I'm always focused on, and this is way before AI, right, or anything. And I've always been in tech, so my whole life has been in tech. I always said hire adults and hire people that can solve problems because a lot of people make mistakes, they hire for skills, right? I'm gonna hire somebody that knows this programming language, right? Somebody who knows this tool.
And you go, if they're smart, they can learn the tool. But if they're not smart and they know the tool, what are you going to do when the tool gets replaced? Or what are you going to do when the new version of the tool comes out? There's a problem. And then another one is the good person. You can teach somebody skills. You cannot change somebody's personality.
Isaac (09:54)
Yeah, no,
I think being able to learn to be a problem solver, to adapt. Yeah, it's essential because also like the business is going to change. like, you know, not only will the tools change, but the business will change. will be unexpected things that happen. You don't know what your roadmap is going to be in six months. You might have an inkling, you might have some long term goals, but there will be unexpected things that if you've hired, if you kind of overfit yourself for one tech stack, one problem set.
Zhenya Rozinskiy - Mirigos (10:05)
⁓ Absolutely.
Isaac (10:21)
you're not gonna be able to be flexible.
Zhenya Rozinskiy - Mirigos (10:23)
So one interesting thing that I've noticed, because we do hiring internationally, right? We do a little bit in the United States, but it's insignificant and it's not something that I... We do this because our clients ask us to and so that's what we do. It's not our core business. So all of our hiring is Latin American and Europe, Eastern Europe, Europe, right?
Isaac (10:40)
We have
a big Bulgaria office, yeah.
Zhenya Rozinskiy - Mirigos (10:42)
Okay, perfect. So you understand that. And one thing that we've noticed, and I've noticed talking to people and just looking at what people are looking for, it seems to me Europeans are a lot more, European engineers, I should say, are a lot more open to this change and a lot more on a frontline of adopting this change. So they're looking at this as the next engineering challenge on how I'm going to do this better and how I'm going to use that.
as opposed to some people here in the US are still very set on their old ways and want to keep doing this. I wonder if you're seeing the same thing.
Isaac (11:14)
Yeah, I so I feel like I can't speak too much to sort of comparison because like our whole engineering work with exception of like data engineering lives in Bulgaria. think, yeah, there's, think the, like our engineers over there, they're great, they're awesome. I think they're definitely interested in like using more AI, learning more about it, using it to enable improved developer productivity. Yeah.
I don't know, sorry. I feel like it's a little bit of a bad non-answer. But yeah, I don't know. I don't think, I'm not sure. Yeah.
Zhenya Rozinskiy - Mirigos (11:41)
Okay, that's fair enough.
So I want to come back to hiring and the future. I have a 17-year-old son who is a junior in high school. So this summer, we're going to be going through applications, colleges, everything. The good news for me, he is not into computer science, not at all. So that's sort of like,
Isaac (11:55)
And that's what we do, just doing all of it.
Okay.
Right, right, not your problem.
Zhenya Rozinskiy - Mirigos (12:05)
But
what would you recommend? And again, I'm asking this because you are someone recently gone through that, right? What would you suggest? One thing, and before I ask that question, I'm actually gonna make one comment. What I'm seeing is less and less hires for junior roles. And you said you guys recruit out of college and applaud for that because a lot of people are going like, no, we don't wanna recruit out of college. In fact, one of the companies that I know,
they had a very extensive internship program and they canceled it this year. So going into the summer, they canceled it because they said everything that interns did, we've now automated or AI is doing this, so we really don't need that. And that raises so many concerns and so many questions for me, like who's gonna replace senior people if you don't have junior people, right? What's your suggestion?
Isaac (12:51)
So yeah, mean one, like I've said before, I think it's scary. I don't think there's like a silver bullet here. A few things. One, I think there is, I think it's a bad idea to stop hiring junior people. Like I just think from like a pure business perspective, it's a bad idea. The reason why is that if you can find good people and train them well, you can effectively like get a killer senior engineer for below senior engineer rates.
Like, I think that we develop people really freaking well. I think that we take people who don't know what a database is, they don't know how to write SQL, and we make them really strong data engineers, really strong data analysts within the course of three to six months. If you invest in training people and developing them, then yeah, like actually hiring people who are smart, who are right out of college, who are junior, like that can be great. In terms of like taking it from the other side from the...
from the student perspective, I think it goes back to that same liberal arts thing.
If I, I've had this thought before that if I was going to like run a school, I don't think I'd ever do this because it's like, yeah, I don't think I'd ever do this. But basically if I was going run a school, I'd force every kid to double major in the humanities and the sciences. Like I think you need left brain and I think you need right brain. And I think that like being someone who's a deep humanist, who thinks about the world, who thinks about people, who thinks about social systems, who thinks about systems on their own.
And then also someone who can do quantitative analysis, who can like do math, who can code, who's literate. I think actually like programming is kind of literacy in the 21st century to some degree. Like, you know, don't need it, but I do think, you know, just having that ability is gonna make you stand out, even in a world that is more more AI heavy. I think having both is the way you insulate yourself from...
Zhenya Rozinskiy - Mirigos (14:24)
Mm-hmm.
Isaac (14:36)
a of the crews because we're not gonna stop hiring 22 year olds. Smart 22 year olds are an amazing resource and they're not gonna stop hiring them. The rules are gonna be different. Who knows what they're gonna be? I don't know what they're gonna be, but they're still gonna get hired. I'm not too worried. mean, absent true AGI singularity, humans go bye bye. We're gonna keep hiring smart 22 year olds.
Zhenya Rozinskiy - Mirigos (15:01)
No, of course, course. Cool. All right, Isaac, this was incredibly interesting. Your energy and everything. I'm really glad we talked. This was awesome. Anything you want to add? Anything you want to say before we say good-bye?
Isaac (15:12)
final thoughts, I guess, I guess, I guess getting back to the kind of like, you you, you said at the beginning how this is a podcast that's more interested in how technology integrates with business than technology for its own sake. think one of the reasons why I've kind of loved working in data, and loved it in general is that using, using data to basically informed.
automated, informed automated business decisions at scale can become a really big differentiator for businesses. I think that like, if you look at Amazon, if you look at Facebook, if you look at all of the big tech companies today, the fact that they have this giant moat of data they've collected, that they can really precisely understand, you know, for good and for ill, precisely understand their customers is really important. And I think I like working in a place where I know that
the work that I do is going to really directly inform business outcomes that I'm kind of, touching money, so to speak. ⁓ So yeah, think it's a, I think there's a really exciting space. I think I'm excited to see where it evolved over the next 10 years. And yeah, no, it was super nice. Thanks for having me on. Thanks for having me on.
Zhenya Rozinskiy - Mirigos (16:03)
Of course.
Thank you. Thanks Isaac.
Isaac (16:17)
All right.