Today, I had the pleasure of interviewing Dmitri Mirakyan. Dmitri Dmitri runs the consumer product analytics team at Opendoor. Prior to that, he was a data scientist, analyst, and consultant at Deloitte. He is passionate about data viz and finding 80/20 solutions to open-ended problems. Outside of work, he enjoys building questionably useful side projects and going fast on motorcycles or skis. In this episode, we discuss how he cold emailed his way into a data role, how he is working on automating his dating and professional life with GPT3, and the trade-off between fulfillment and achievement. I learned a lot from this chat with Dimitri, and I think you will too!
Transcription:
[00:00:00] Dmitri: Actually went back and I just asked, like, I didn't say I had anything else lined up. I just like put together a one page doc saying, Here's a thing that I think I should do and here's why I think it's useful for you guys, and here's why I think I do really well at it. And was very surprised when the answer came back. Yeah, sure. You should give it a go. And now I manage the consumer product decision science team.
[00:00:34] Ken: This episode of Ken's Nearest Neighbors is powered by Z by HP. HP's high compute workstation-grade line of products and solutions. Today, I had the pleasure of interviewing Dmitri Andreyevich Mirakyan. Dmitri runs the consumer product analytics team at Opendoor. Prior to that, he was a data scientist, a data analyst, and a consultant at Deloitte.
He's passionate about data visualization and finding 80/20 solutions to open-ended problems. Outside of work, he enjoys building questionably useful side projects and going fast on motorcycles or skis. In this episode, we discuss how he cold emailed his way into a data role, how he's working to automate his dating and professional life with GPT-3, and we also touch on the trade off between fulfillment and achievement.
I learned a tremendous amount from this conversation with Dmitri, and I think you will too. I hope you enjoy the episode. Dmitri, thank you so much for coming on the Ken's Nearest Neighbors podcast. We got introduced to a mutual friend at Jeff Lee, who's been one of the very few two time guests on my podcast, and it seems like your story is absolutely fascinating how you broke into this career.
Sort of how you were a digital nomad for some period of time, and we're also kindred spirits in that we, we had some interesting experiences in consulting to begin with. So I'm really excited to dive into your story here and the introduce you to the Ken's Nearest neighborhood.
[00:02:01] Dmitri: Yeah, man, I'm super thrilled to be here. This might come up later, but yeah, like full credit to you for one of your YouTube videos for actually getting me into data science at one point. So kind of a crazy small world.
[00:02:17] Ken: Yeah, well, I'm very grateful that it came full circle, right? I mean, we talked a little bit offline about manufactured serendipity and how we sort of make our own luck and I guess me putting some content into the world looked out for later me to have awesome guests on the show.
So, it's funny, funny how everything pans out like that.
[00:02:38] Dmitri: You can probably take your data science chaps and extrapolate from like you running into me, how many other people have managed to get into data science via the sampling based on that same video, so who knows.
[00:02:51] Ken: Hey, if anyone wants to actually run that, that market sizing analysis and report back to me, they're more than welcome. In order to maybe get everyone a little bit more familiar with you, I really love to start with learning how you first got interested in data. You know, was that a pivotal moment? Did something happen or was it more of a slow progression over time?
[00:03:14] Dmitri: Yeah. So I think, I don't know. I've got this kind of annoying theory which is that most of the time people say that like, Oh, this is my life and this is what I've been destined to do my entire life. They're like doing revisionist history and kind of like modifying their resume to see how it all led up to this point. So I'm gonna do the opposite and say I got into data completely by accident. Which is to say I always wanted to build stuff. I studied mechanical engineering but didn't realize I didn't want to build stuff for a period or ExxonMobil, which seemed like what mechanical engineers in my school did at the time.
And I got into consulting, which is what I think a lot of smart people do when they don't know what else to do. And there I built slides and that was something I did not enjoy doing. And then I started doing UX and graphic design, which was a fun thing to build that through more serendipity and getting people to like what I did, got me on a project where I actually finally started doing analytics.
So we, we hosted this giant company wide conference called Deloitte Disrupt. And I had the idea of, Oh, we should run the numbers on it. And then I. Hacked some stuff together so that we could actually get tracking and started running the numbers on it. And that went awesome. And then we did it again.
And that led to more projects doing analytics. And at some point it was just like, hmm, you know, it would be really funest to do this full time instead of spending most of my time making slides and just doing this like in my free time.
[00:04:52] Ken: That's awesome. You know, it's, I think it's I view a lot of the decision decisions I make as a greedy algorithm, right? I look at what the best opportunity is for me based on the opportunities I currently have, and I just take the best one that I think has the largest total payoff in aggregate. And a lot of that time that leads me towards things that I'm very interested in, but also I have some aptitude for some exposure to, and if, at least from my perspective, when I look at it that way, it's not like super risky because there's no way to know.
Which direction is better for or worse? Maybe it's some sort of modified greedy algorithm where I'm not just pursuing like hedonistic things, but like, it, it's like I view it as what is the largest upside long term for, for any of the decisions that I make. And I always am fascinated with people how they, they, you know, they, they had to have some sort of experience.
They had to put these feelers out to be able to understand what you like. And then if you understand what you like a little bit more, you can make some decisions or you can move towards a specific goal. So what, what, in your consulting work, obviously we talked a little bit about how maybe it didn't start out as well as, as well as it could have.
What, what pushed you towards that analytics? Or like, how'd you even get that I idea the start doing the analytics on that conference?
[00:06:13] Dmitri: Yeah. The, so like both a push and a pull. I think I'm very line on the perspective of greedy algorithm. I've thought about it as just like incre mentality over the next year or two.
Cuz thinking 10 years out is kind of tough, like over the next year or two based on all of the things that I could see, like where could I throw my energy to propel myself forward. And so from the consulting angle, there are two things. One, it turns out that I actually sucked at the base job.
Like I just, I did not do well on my first couple of projects. Turns out that like taking notes and meetings and making fancy presentations for clients was just something that was not fun. And actually did worse on that than like a lot of the peers that were around me before in school. And then two kind of taking a lot of projects and discovering actually like really a year into my consulting career, discovering analytics of the thing that took advantage of my problem solving skills.
Admittedly, in like one of the weirdest moments possible I was doing, I was doing analytics for a toxic waste management client, literally doing metrics around like, has biohazard waste. And had one of the, like you ever have one of those moments where you get so excited about an idea that like, you can't put it down and like you end up just staying where you're at and working on it until like 1:00 AM.
[00:07:52] Ken: Yeah. Oh, I've had plenty of this.
[00:07:55] Dmitri: Yeah. Yeah. Like I had that moment where this was for like a work project and I had this idea and I saw where this was going and I just could not put it down. And I just ran through it. Got something done in a couple of days that, yeah just feeling that rush. And then from there was a pretty straight shot.
[00:08:19] Ken: That's awesome. You know, so. Go ahead. Sorry.
[00:08:21] Dmitri: No, no, no. From there it was a strange shot. Like, I want to do this more, Let's do more of this.
[00:08:27] Ken: Let's do more of this. and searching for those types of experiences is something I love so much. I think that I didn't love my experience in consulting and granted, I still do consulting, but I have a lot more control over my projects than I...
[00:08:42] Dmitri: But there's a difference between you do consulting for like a big consulting firm and like you do consulting, like somebody hires you to do consulting and you doing good addressing problems. Biggest thing, like you get to pick your clients ... office..
[00:08:55] Ken: That is a big, a massive one. I will say I really did like about it that I got to work with a lot of different clients and I got to figure out very quickly what I didn't like. And some of the things that I did like, well, I learned that I liked to code because when I had downtime, I would just go on freaking Code Academy and just do that all day and like kind of shook off some of my work.
I wasn't the best employee when I was a consultant, but. You know, that, that to me is such a valuable experience of just getting these reps and seeing the things and building this model of the world that can guide your direction. So many people just like take the old philosophical approach if they think they can think their way into everything.
And to me, that's simply not the case. You have to experiment your way into everything. You, so thinking and being introspective is important, but you have to have some framework to base those decisions off of rather than just going in it with like, Oh, the world is my oyster. I could do anything, could be paralysis.
[00:09:52] Dmitri: It exposes you to a lot of things. It also gives you a ton of optionality because for better or for worse, and bunch of people will hire X consultants. And it, it definitely showed me just a massive amount of things that I never wanted to do. Case in point, like experience of seeing most of the clients that I worked for is, Holy crap, I never wanna work in your average office in corporate America, this sounds like absolute hell. Yeah. Sorry, no disrespect to anybody in law along the way.
[00:10:23] Ken: No. Well, I, you know, I realized something about myself. I went into consulting, thinking about the exit opportunities, and I don't think you should go into a job. I wouldn't now go into a job thinking about how I would leave and what I could do afterwards.
That's not necessarily the best, the best stepper process. It, it can have utility, but for me, I don't think I maximized on what I could have done if I had, if I had experimented more, maybe in school with internships or some of these other things, I really would've I think, been able the find something where I was more excited about it that would've li led to just as good exit opportunities. But I digress. I am interested in those analytic skills and like how you develop them. Were you just cranking things out in Excel, or what, what were you doing in these early projects?
[00:11:15] Dmitri: Yeah, so. I can talk about the fun stuff and I can talk about the real project work. And the fun stuff literally started with me first is like doing graphic design for people.
It turns out that like if you're a builder and you're the kind of guy that likes making things, at some point you make just about every sort of thing. And at one point the thing that led me to this project was I was literally making custom graphics and an icon. The thing that like got me into the project was making a custom camel icon for a project whose entire brand and theme was a camel.
Somehow. It was like, it was an agile project and their mascot was a camel, and they were just so thrilled that I created this Deloitte custom camel icon for them. And then when we started doing the actual show, it was. Oh, I want to do analytics. Well, I guess I gotta get a Tableau license. And I got a Tableau license.
So then I started doing different, different data visualizations. And when it came to my actual project work it ended up also still very much bi. But the question was how do we show operational analytics from like the lowest level of an individual to managers, to regional managers and how you design it in a, in a meaningful way.
And then I ended up doing mockups at PowerPoint and then actually going and downloading the software and just seeing like, okay, cool, what's possible here? How does this work? How can we make this work? And then actually showing my mockups to the engineers when they disagreed with me and saying, This is what I want.
This is how this needs to work. This is the mock data. This is, this is the thing. Because I think after a couple of long nights of banging your forehead against something and being really excited about it anyways.
[00:13:13] Ken: If you were one of those engineers now having that conversation with you, how much would you have hated yourself?
[00:13:21] Dmitri: I'd be really annoyed. I'd be like, There's nothing more aggravating, I imagine, to like any other human in the world than like an overly excited 21 year old that clearly has no idea what they're doing, but this is the hell that they're gonna die on. This realistically yeah. I don't know that that was the first time doing this stuff. I can't imagine it was actually a good idea.
[00:13:47] Ken: Yeah, you never know. We, we all, we all have some, some highlights from, from back in the day that you're like, I, like, I've read a paper I wrote and I was like, There's no way I wrote this. And then I've read other papers I've written and I was like, I definitely wrote this.
This is trash. Like, it's a very hit or miss thing that. We look back in retrospect, I, you know, you brought up the idea of the like builder mindset. I'm interested if you can expand upon that. Like how did you know that you were a builder? What is the alternative to being a builder? Because I also view myself as, as a builder now, but probably not in the conventional way.
[00:14:25] Dmitri: Sure. Being a builder versus the alternative to being a builder. So I think if I think about what makes me tick and what motivates me on any morning I think back to, and God, this is, this is gonna sound really ridiculous. This is, this is gonna be the paper that I look back on and I roll my eyes and I was like thinking about like myself as a four year old that just drew a picture and is running around showing it to everybody and showing it to my mom and my dad, and my grandma being so excited about this thing that I created.
And so thrilled to show everybody this like thing that I made. So I started out as an art kid and then I discovered physics, and it turns out that like, Ooh, I'm really good at that too. And then I realized, Oh, what if I made like actual things? And there's a, I've heard this argument made before, I don't quite remember who says it.
Maybe it was P Graham, maybe it was somebody else that was like, find the areas where you have an unnatural advantage and like find, find places where you have an unfair advantage. And I think I discovered at some point that my unfair advantage was I like to make things. I'm just like a creative person.
I like putting things together. It gives me joy. And two, I happen to be better at understanding systems and like breaking things down and figuring out how to make things work. And a lot of people around me and. That naturally led to, Okay, cool. I can go into just about any space and get your joy out of figuring out how to disassemble something, put it back together, make it work better.
Like not, not being the guy that discovers brand new ideas, but being the guy that takes a bunch of Lego bricks and puts it together into something useful. At some point that like expanded into like, Oh, I actually like building teams and I like building systems, but at the core level, at the like, core of what I do is still I love that like, feeling of getting so excited about something.
You go into the zone and you stay up all night and in the morning you like get push and there's something like you've created life. Like there is something that exists in the ether that people are interacting with and people that are seeing that you created. And I honestly don't care what it is as long as it's like exciting to somebody else and exciting to me, just creating a thing is the most satisfying thing for me on like a near a level.
[00:17:06] Ken: You know, you made a relationship or connection between art and engineering or whatever it might be that I never would've made in the past. The idea that like both artists and programmers or data scientists, they're all, they're creators.
Like they're buildings, they're builders, they're putting something into the world and I've always felt some sort of like connection to the art community or to the creative community maybe cuz. Freaking videos on the internet. And that's like a product information that, that, that I put out there. But to me, I could never put a, put a finger on it, like, what was that relationship?
And I think it is that those two groups are inherently interested in building and sharing things like we, we do data science. We, we work in this domain to build things and to share them, whether it's analysis, whether it's an algorithm, whatever it is. And I think that there's something really like special about that.
And I also like that there is that relationship between the more purely creative side and the more what people would consider like systematic and engineering focused side. That maybe they're not as different as a lot of people make them out to be.
[00:18:15] Dmitri: But there's also something beautiful about the fact that you can do everything a million ways. I think the thing that first blew my mind about physics is like you can take every problem and it doesn't matter which steps along you took along the way as long as like, as long as you get it right, Like you can take the same thing, disassemble it in 20 different ways. And if the end result comes out correct, then you're still correct. And it's so, it's, it is so satisfying that you've got this entire canvas to work with. I dunno, it feels like a video game.
[00:18:45] Ken: Yeah. Well, you know, we talked a little bit about that offline, about how coding, in some sense, if you look at it the right way, it, it sort of does behave like a video game. Can you maybe expand on that a little bit more and maybe your experience with coding?
[00:19:00] Dmitri: Sure. So maybe this was the piece where with Deloitte, when it first hit me, I was like, Oh, this is fun. I want to engage with this. Coding feels kind of addictive on almost a chemical level. Kinda. If you look at it in comparison to video games actually my, my first manager opened door and the person that got me into Python, which ultimately led me into data science described this as coding as like video games for adults.
And this applies to sequel too, which is, it just compared to so much other creation, it just has that like instant dopamine drip of you run something and sometimes it works and sometimes it doesn't work, and you can build it very iteratively. There's no measure something 30 times to get an end result.
You're like constantly building, constantly iterating and just able to like very quickly see something come to life. And it's kind of an incredible feeling that, like co corollary to that is that the reward is often variable, which I think on a certain level makes it more addictive. Cause you don't know if it's gonna work or not.
And so you just keep on sitting there like pressing the lever. And every once in a while it spits out a reward. And finally at the end when you've created something useful and you deploy it just like all the entire rush at once it certainly feels like the most chemically addictive thing that I've ever done for work. Which feels like a wrong thing to say about something positive, but.
[00:20:40] Ken: Well, you know, I mean, I think that's okay. I think it's definitely a good thing if you perceive it and you view it that way. One of the challenges I think a lot of people have is the level of challenge that they're pursuing is either too easy or too difficult.
And I think coding was very much a sweet spot for you because it met the level of challenge where you're succeeding at the right amount. Like if someone goes and they try to start, you know, they open a advanced Kaggle workbook, for example, and they'd never written a line of code, it's like, well, this is not gonna be fun because there is like, nothing works that I put in.
Right. and building a system for yourself, especially when you're learning that matches what level you're on, is what gives it that addicting and sort of gamified level, which is something I strive to do is like, if the challenge is like a little too hard, I'm like, Oh man, I'm just gonna push this off. Or if it's a little too easy, I'm like, Man, I'll do this tomorrow. It's just so easy. I'll just like, it'll take me 10 minutes. And I'm like, Damn it. Why didn't I just do it? But...
[00:21:43] Dmitri: There's actually a data scientist at Opendoor that like said something that really stuck in my head, which is I asked him what kind of problems he like to work on when he first joined. And he said, Hard, Doesn't matter what kind of problems they are, they need to be hard. And that just really burned into my brain. Like interesting, critical thinking does correctly, needs to hurt a little bit, like a good workout. And if it's not, then I'll start trying harder problems. And that's a. Sorry, I didn't mean to...
[00:22:15] Ken: No, no, that was perfect though. That to me, that's really good advice, is to find the challenge that meets you and like your appetite for how difficult the problem is, is something that factors into that as well. Which is, which is I think something that, that we perceive, like, like what is hard?
It's a subjective measure and my level of difficulty versus someone else's level of difficulty. That's something that I should be able to adjust in theory. Right? Like, why is it hard? Is it hard? Because it'll take a lot of time. Is it hard because it's complex and I have to do a lot of research? Like what, what are the factors that fall into that?
And I personally believe that people that seek challenges like that are the ones that have the appetite or like that those challenges match their level of skill very, very evenly.
[00:23:04] Dmitri: It kind of goes back to the greedy algorithm thing. Like you figure out like, You push a rock up a hill and then you sit on top of the hill and then you look for the next hill to push it up.
Like, like take a break for a tiny bit, like rest your arms. And then, and then you start pushing. At some point you look down and it's a long way down. And then you stop doing the self aggrandizing stuff and look for the next hill to bush or rock up. I don't know.
[00:23:33] Ken: I really enjoy that metaphor. Something that you explicitly said is you look at the hill you've traversed. I guess that's not something a greedy algorithm does, but as humans, I think is really important is to look on the body of work that you've created. Because when we get sort of stuck in these, in my opinion, like the greedy algorithm loop, you feel like you haven't gone that far because you're always looking at what's in front of.
But when you do take that perspective and you look back, you're like, Wow, I've actually done a whole lot. Like the other day I looked and I was like, Oh, I've done over a hundred podcast episodes. I've done over 200 YouTube videos. Like if you had asked me how many I'd done, I've done, I wouldn't have said that many, but I'm like, actually that's like, that's like hundreds of hours, maybe thousands of hours of content to me.
Like I was astounded at the amount of, this isn't like me bragging, but it's like I was astounded at how many things I'd produced and it's like a nice feeling to be like, Wow, like I've done that much. Like what more could I do? There's so much more on the other side of this.
[00:24:35] Dmitri: Yeah. Also, like, I don't know, we're humans and humans have anxiety and anxiety turns out to be kind of counterproductive to pushing rocks up hills. So taking a quick break to look down and be like, Huh, I'm doing all right. Yeah, it is. It is a nice view.
[00:24:52] Ken: Yeah, especially years back there. Geez.
[00:24:54] Dmitri: Literally love New York.
[00:24:57] Ken: This episode is brought to you by Z by HP. HP's high compute, workstation-grade line of products and solutions. Z is specifically made for high performance data science solutions, and I personally use the ZBook Studio and the Z4 Workstation. I really love that Z workstations can come standard with Linux and they can be configured with the data science software stack. With the software stack, you can get right to work doing data science on day 1 without the overhead of having to completely reconfigure your new machine.
Now back to our show. So, you know, talking about how far we've come, I'm interested in how you bridged that gap between. You know, consulting to data analysts, to data scientists, to now like managing a team. What is, what does that look like? How do you actually go and have that type of career growth?
[00:25:47] Dmitri: Yeah. So definitely I think who's been on your podcast twice now actually has a pretty good either blog post or video about this. I didn't see it at the time, but I had a friend that kind of explained this method to me, which is looked at all the places where I wanted to be. He, he's, he's at me down and said like, This is how you go recruit.
So I looked at all the places that I wanted to be, like all the places that I could be. I actually like looked at everybody else that was a Deloitte consultant and I looked like where they went next. I. These things seem interesting to me. And then I just started sending out cold messages on LinkedIn first to like understand what it is that I wanted to do.
I was just like asking for people's stories, asking them about their careers and trying to make sense of if this is what I wanted to do. And then following those stories I started essentially asking people for referrals into jobs that were available at the time. And I think the role that got me into Tech was reaching out to 15 different people at Opendoor via LinkedIn.
Two of whom replied to my message, one of whom I talked to and who referred me into the role. There's a funny moment there where like actually after I talked to him, I applied for the role directly and within a half an hour I got rejected. I got like a polite rejection email and then 15 minutes later I got a follow up saying, Hey, actually this person sent your name.
Let's talk. And. That got me that got me into my role at Opendoor where I started out as, as an analyst. About six months into being an analyst, I was like, you know, SQL is cool, but you know what else is cool? Python? I had a manager that explained to me that coding is like a video games.
And also we used Mode Analytics, which had access to Python notebooks. So I just started building weird stuff like during work and outside of work with Python. And then at some point, again, serendipity, crazy stuff somebody was like, Well, you know Python and you can do this stuff. Why don't you go be a data scientist?
And they got me in touch with the data science team. They were like, You're not sure you wanna do this here, Just interview. Let's see how it goes. And they sent me some machine learning home. I discovered your videos online. Spent three days, took an extra day off, spent three days learning machine learning.
Literally had the Python notebook side by side next to your YouTube videos and was learning it as I wrote it. And then managed to do well enough that I got into the data science team which was a blast. Spent a year doing data science. And then actually ended up having an opportunity to do something else that I thought was really cool.
And then ended up asking myself like, what would it take for me to stay where I'm at? And the answer was managing the product analytics team. And I actually went back and I just asked, like, I didn't say I had anything else lined up. I just like put together a one page doc saying, Here's a thing that I think I should do.
And here's why I think it's useful for you guys, and here's why I think I do really well at it. And was very surprised when the answer came back. Yeah, sure, you should give it a go. And now I manage the consumer product decision science team is a team of decision scientists that runs the consumer product analytics at Opendoor.
Again, I absolutely don't trust any of the people that say my entire 20 year life was meticulously planned, and I had my vision from the start. Feels like everything happened by accident with the one exception being like constantly trying to push rocks uphill and doing weekend projects and building stuff, but, well.
[00:29:58] Ken: You know, I, well probably some of the best advice that I ever got from anyone, I don't remember who it was, which is a little bit of a shame. I'll attribute my dad and he probably stole it from somewhere else, but it's the worst that someone else can say is no. Like, why not ask? I mean, don't ask stupid questions. Do your homework. Yeah, do your research, whatever it is. But you know, if you ask for what you want, you'd be really surprised when people say yes.
Like I had a friend every single time he went Chipotle, he would just be like, Hey, can I have this for free? And one time he got it for free, actually, a couple times he's gotten it for free. Like, like, you know, what's the harm asking? Like, No, they laugh or whatever it is. You might get a day where the person's just like, you know what? Yeah, sure.
[00:30:40] Dmitri: Is this the life hack of the podcast? Always ask for free walk. And every once in a while you got free walk.
[00:30:45] Ken: Yeah, man. I mean, I mean, like, again, what is the downside? They're not gonna kick you outta the Chipotle. They're not, they're like, they'll probably just say no. And if you can handle rejection, if you handle the no's, I mean, why not?
I mean, it's the same thing with, you know, dating or any of these other things. If you're like talking to someone, it's like, Oh, you know, like I want to talk to this person. I like their vibe or whatever it is. If I go up and talk to them, there's unlimited upside. Like, Oh, I, you know, I might date this person, I might, whatever it is.
But on the downside they say no, I probably will literally never see them again. Like the downside is so hedged, unless you're making a complete fool yourself, but.
[00:31:25] Dmitri: The other plus is like, once you spend a lot of time asking people for free walk, you get really good at asking people for free walk. You like gonna increase...
[00:31:35] Ken: ... A/B tests.
[00:31:37] Dmitri: I came with a shot, man, it's especially a weird thing cause I feel like the average person in data science and analytics to the whole, like, if you had to plot a chart of like where people sat in the high school cafeteria, people doing data science are sitting around the far, far opposite corner of the people that end up doing sales.
Like those people do not share a table at high school lunch. And I so this, I don't know, it definitely didn't come naturally to me, but yeah, it's frustrating how well the whole like, just ask, ask or ask, just ask. But thoughtfully and sometimes like a half pager of why you'd be really good at it helps. Yeah, I absolutely agree.
[00:32:16] Ken: Well the half pager is, is something that I really liked. Jeff actually talks about this, this is where I first got introduced to Jeff. He did a interview, I think it was on, like towards state of science. And he talked about the briefcase method. So you essentially go into an interview with a half pager, one pager, The projects you'd like to work on at the company and how you could fulfill them or whatever it is.
But you're, you're coming in prepared to the interview, something you thought about, the problems I work on, whatever it is. That can really go a long way, you know, even if it's just a little bit, I mean, you forget that you're being compared against the baseline. And if literally no one else is doing something like that, or no one else is in the role, or no one else is eyeing position or trying to make advancements in that certain way, you stand out in a very aggressive way from, from the non-existent pack.
[00:33:08] Dmitri: Yeah. the extra effort goes a long way. I love the brief briefcase method. After watching that entire video, I feel like I stumbled upon the same thing kind of by accident. In a way it mirrors what I feel like a cover letter would've used to be, except cover letters are kind of useless now. Like, talk about the first thing that can be generated with ai.
[00:33:31] Ken: So I think everything is useless unless you make it useful. So I had a couple cover letters that I wrote that a hundred percent got me an interview. Really? Yeah. I wrote one I was applying the lead one of the data teams at, it was a big, essentially like like ski resort company. And I would go, I used to go skiing there every year when I was a kid.
And so I wrote about the time where I like got lost on the mountain and like, like it was, it was like a short story. It was incredible. But I was not qualified for the role and I at least got a call back after that one. But you know, if it's fundamentally different from what people are, are seeing or viewing and not necessarily in a bad way, even if it's like unbelievably cringy, I think.
You know, it's the same thing as like, why not ask, why not try, why not experiment? Maybe I wasted an hour of my time just like writing something weird. But like, I could probably find that now and publish it somewhere and like put it on LinkedIn and get some, some like traffic or something along those lines. Who knows? .
[00:34:36] Dmitri: Yeah, it'll be a nice motivational story. Maybe can like make an ink spray. Yeah. Inspirational LinkedIn post about how.
[00:34:42] Ken: Yeah, I mean, if I got the job it would've been a lot more inspirational, but.
[00:34:47] Dmitri: But nobody knows that. Like what percentage of length? Like I feel LinkedIn post has gotta be 90% made up. Probably at some point you just gotta add a kid in there and then a dog that says an inspirational quote. And then I had to do golf for like LinkedIn.
[00:35:02] Ken: I mean, I'm not sure if you're on the LinkedIn Lunatics page. Love it. It's, it, yeah. I've had quite a few friends featured on there and it's actually Zach Wilson who has been on the podcast was, has been on there pretty aggressively.
[00:35:16] Dmitri: ... you considered taking that direction for yourself, like becoming a LinkedIn influencer as opposed to giving people tutorials to get them into data science? What if you just inspire them instead?
[00:35:28] Ken: I mean, I could, I have almost a hundred thousand followers on LinkedIn. I just repost my YouTube stuff, but I'm not, I don't like the text medium. Personally, I don't like writing stuff because people can reread the stuff you write really easily. It's really tough for people to just re rewind, like to rewind a video five seconds to watch me fumble over words over and over again. Right. Yeah. Like, to me, that's just like some weird bear to entry or like some, some like mental hangup that I have. I've written some blog and stuff, but I'm, it takes me longer to write a blog then to record a YouTube video by a long shot. And so I try to focus on what my strength is in that case.
[00:36:07] Dmitri: Yeah. Maximizing would be roi. Yeah. I wonder what percentage of those, like LinkedIn and inspirational Twitter post are just procedurally generated. Like there's gotta...
[00:36:17] Ken: You could find, you should, that would be a good project for you to like create like BS, LinkedIn posts and make a LinkedIn bot that just like every, every week I'd go in on that with you.
[00:36:29] Dmitri: Somebody created the LinkedIn one. I kind of want to create a Twitter version, but there's a viral post generator.com. Did AI for a LinkedIn post and yeah, that honestly, it's kind of indistinguishable from the real stuff. Somebody posted an article to Hacker News the other day and it got a bunch of up votes and then two days later they posted an article saying, I published a procedurally, like an AI generated article to Hacker News. And it got upvotes, it's the hack on years and I got outputs. And it was like self-help like kind of inspirational. You should believe in yourself type of stuff.
[00:37:05] Ken: Interesting. Well, you know, on that note, I'd love to hear more about some of the projects that you're working on, some of the ones that you've done in the past. I know you, you have a knack for doing. Just like, I think the right word is quirky projects that are also fun and have some utility in your life. I would love to hear some of the details on those and what other people can maybe take away from them.
[00:37:29] Dmitri: Yeah. I like how you use the term quirky. I think I started out using the term value negative projects asterisk being that like I've done a ton of stuff like at work and otherwise that is useful to a bunch of people and that like, has helped generate value and increase profit and reduce costs.
But like, but it's not funny and it's not, it's not nearly as entertaining. And it turns out that you can have a massive amount of fun when you're doing things that are abjectly useless. So some of my, my favorite.
[00:37:59] Ken: Is it useless if it creates utility and humor for yourself, though.
[00:38:04] Dmitri: It creates humor for myself and I like to think it improves retention within my team. By entertaining them and making them feel part of the community and also demonstrating a certain level of technical excellence, like, huh, this is utterly useless, but it actually works really well. Wow. You even wrote tests for it. Incredible. I think the first useless thing that I created, my current place was a a randomizer for internal links.
You know how like a bunch of companies have like an internal links shortener. You type in like, like effectively tiny URL or go slash whatever. We had a proprietary version of that and I built a randomizer for it, so it would send you to a random internal link. Which is honestly utterly the worst thing that you can do for a corporate one, because you're just wind up seeing random documents from three years ago that have absolutely no context to you.
But yeah went in, spoke to the engineers that ran the repo, told them, Hey, I want to do this. Here's a pull request, and they're like, Oh, this is great. And that got implemented. At some point I got tired of being a manager. And at that point I was playing around with G P T three, like some of the models that have come out recently that have generated that are used to generate things like fake LinkedIn posts.
And I was like, Can I outsource myself being a manager? And so I found a way to plug it into a Slack bot and just host it an online and got it to answer questions for myself and my team. Had a lot of fun playing around with it. My grandparents got a massive kick out of it and testing it on my siblings turned out to be not very useful. As a reference. Anytime somebody asked it for pto, it would reply decisively with no. But it was, it was entertaining.
[00:40:01] Ken: I guess. That's what GPT-3 thinks a good manager does. I or the average manager does. Who knows.
[00:40:08] Dmitri: Honestly. Yeah, that's maybe, maybe that's the secret. And then what was I gonna say? The most recent thing that I built was I got tired of using dating apps and messaging people on dating apps. So I actually took some information from Jeff again in that regard. And I built an app online that uses AI to generate responses and openers for dating apps. Just launched it like a week or two ago on yourmove.ai. And that one's proven slightly more useful but still mostly just entertainment.
[00:40:48] Ken: I will link it in the description of the video. We'll run up through GPT-3 bill a little bit.
[00:40:53] Dmitri: I may or may not have used on people. We'll see. It's great.
[00:40:58] Ken: So how does someone respond when they realize or you just never tell them?
[00:41:02] Dmitri: I, they mostly just ghost me. So I've been seeing a person and I can't actually do anything with these messages, which is fun fact, really great conversation to have with somebody that you're seeing is, Hey, sorry, but I need to have all of these dating apps and active conversations because I'm working on this stupid side project. But yeah, when I tell them I'm sorry, I'm not actually looking for anything, I was just testing this terrible ai I mostly just get ghosted.
[00:41:31] Ken: I mean, you could always just make an account for like one of your friends if they're too lazy or whatever it is, and just use it for them.
[00:41:38] Dmitri: I think a couple of my friends are actually using it every once in a while, which makes me kind of afraid. But yeah. It's been fun also at asterisk. Lots of useful projects along the way. Just feel like I shouldn't talk about those in way less fun. Is that the principle of. Spent twice as long automating something as it actually takes to do the think. And I've fallen for that quite a bit. Like, why do something manually in four hours when you can spend 12 hours finding a way to automate it?
[00:42:14] Ken: Hey, automation in theory scales. You know, if you're trying to date as many people as possible through dating apps, in theory, you're maximizing. I mean, you're the current person you're dating probably wouldn't love that, but just let her know you have options. If, ..., I'm just kidding.
[00:42:33] Dmitri: By the way, if this doesn't work out, I've got an ... got an algorithm. She does occasionally ask me when I send her a message that sounds dumb. Like, did you just use the AI for that? At which point I feel sad. And reply with, No, that was actually me.
[00:42:52] Ken: My girlfriend would just assume it was me. It's not, my, not texting is not my greatest strength. Maybe you, maybe the next one you do is you just build like a relationship bot rather than a dating bot. Cuz you're like slowly moving the progression. Soon you'll be able to automate your entire interaction with, from meeting the other person to actually maintaining the relationship the whatever it comes after that.
[00:43:21] Dmitri: Yeah. So two thoughts. One, at some point I thought about I ended up downloading all of my Facebook Messenger data from the past 10 years because you can do that. And I was planning to train the model on myself to sound like me. And then I realized the last thing that I want for a bot that flirts with people on dating apps is to have that bot sound anything like me, cuz I'm terrible at this.
Second of all, this is a weird thought. At some point I was in a room with a ridge Carati, who is the Tesla data science guy. And I ran this idea past him before I built it, and his reaction was that like, Oh, what if everybody trains an AI model on themselves? And then you run those models past each other and then like evaluate the compatibility based on how different people's AI models like vibe versus them ever having to talk to each other and quote, unquote, find your ideal partner based on having your chat bots engage.
[00:44:25] Ken: Just a bunch of simulated conversations from different people.
[Dmitri] Yeah. You ever watch Black Mirror?
I mean, No, but I'm familiar with the concept. I mean, it makes sense. I've legitimately thought if I could like get all my text data and just see how I respond to certain things. I mean, I have what, like, like hundreds of thousands of texts that I've sent. Probably more.
I mean, it'd be very interesting if I could just create a bottom myself where most questions I could answer or I would at least alert me with some good responses for what I would actually respond with.
[00:45:02] Dmitri: Do you think your reply guys would appreciate it if you if you plugged in an AI bot to respond to them?
[00:45:11] Ken: Probably. Who knows? They, they would, they would appreciate the thought. They probably wouldn't appreciate the utility.
[00:45:19] Dmitri: No. That, that would be pretty interesting. Like imagine a world where, like if you're somebody important, instead of everything going into a black hole or like going to your assistant first it goes through a chat bot that resembles you and then
[00:45:35] Ken: I don't know, people would pay for that service. I bet. Think about it. I mean, who was it? There's a couple people, I think Jim Quick is one, he's like a, you know, an author. He writes about personal improvement and he does this thing where you can text him and then it's obviously not him that's responding, but it's like a, some form of bot probably a decent market for that.
[00:45:55] Dmitri: You imagine like all the people that are doing the like LinkedIn influencer stuff, the people that are just publishing like 30 pieces of content a day, they can't, they can't actually be writing all of that themselves, right? Maybe.
[00:46:08] Ken: Some are, some are, some aren't. I know a lot of people sort of in that, in that niche and it's, I mean it's interesting cuz it is text, so I would imagine some portion of it is, is fabricated, but at the same time all of it is based on, a lot of it's based on engagement and like commenting and those types of things. So it'd be tough to like, to have high confidence in what's going on.
[00:46:40] Dmitri: So this is going way too dark. But have you, and I'm not speaking for this, but have you ever heard of the dead internet theory? No. Was like the idea that, Oh, go ahead. No, no, no. Go ahead. It's this idea that like 95% of the content on the internet is just like bots talking to each other, and there's like 5% of humans in the mix. And like.
[00:47:03] Ken: I mean, from my YouTube comments, I can tell you, exactly what percent is bots and it's high. But I wouldn't say more than like 30% if If I had to guess.
[00:47:15] Dmitri: Probably not. I don't think it's actually anywhere.
[00:47:18] Ken: But video solves that problem. That's actually one of the reasons why I think there is so much more prevalence of video content is, at least right now, it's a lot harder to fabricate in a reliable fashion. Right.
[00:47:33] Dmitri: Well, there's actually a bunch of startups that launched over the last year that are trying to overcome that, which was like, they, they take your face and they take whatever text you've got typed up and they create a video of you saying some sort of speech. Apparently consulting companies are really big fans of this stuff cause it's a fancy version... but that American number one...
[00:47:57] Ken: Proponent of it, McKenzie, I probably shouldn't say that super publicly, but, eh, it's fine. They know what they're doing.
[00:48:06] Dmitri: Yeah. Don't, don't miss that life. I think it was actually KPMG that was big on this thing. Anyways I trust that you and this podcast is not procedurally generated. But I can certify for anybody listening that I so solemnly swear that my voice in this text was not generated by GPT-3. And that this is not a bot. And then this.
[00:48:32] Ken: That sounds like something a bot would say, but... Well, let's, let's change gears a little bit. You know, you did a lot of travel, you lived as a digital nomad for a period of time. I'm interested in how you got into that and what maybe your, some of your coolest experiences were like.
[00:48:49] Dmitri: Sure. Getting into that. Hmm. I'd say it started in college where there was a random moment where a friend of ours from Mexico and a few of our friends wanted to go to Cancun. And our friend was like, No, no, no. You shouldn't go to Cancun, you should go to .... And we're like, where? He was like, You go to ... and then you're in Chapas and then you drive up to San Krista the last Casa. And we're like, Okay, sure. You know what? Why not? Let's do it. We ended up showing up. Our friend showed up three days late.
We couldn't get a car, We didn't know Spanish. We got completely lost. It was like a tiny town in a remote part of Mexico. But it was like one of the best experiences in my life. Just completely wild and insanely chaotic, just but really, really memorable. A lot of type two fun. A lot of like intense memorable moments of like crashing motorcycles and climbing mountains and meeting strangers. And so...
[00:49:57] Ken: Is, can you define type two fun? Is that fun that you realize it's fun after the fact?
[00:50:02] Dmitri: Oh, yeah. Things that seem like they absolutely suck in the moment and that are terrible. And after back you look at that fondly, they're like, Huh, yeah. Nice. Like climbing a mountain or like, I dunno, like going skiing or doing anything else really painful, but yeah.
And then at some point I was remote and I was sitting and I was kind of bored. And I had this realization that if I am who I am based on my values, which one of those was like to live a good story and I kind of needed to go travel for a bit. Cuz at the time there was very little downside for me to go travel besides just like, oh, it's a lot of work, it's a lot of planning, it's a lot of thought and it's kind of uncomfortable.
And the upside was this seems like an interesting thing to do as a person and then I'll look back on fondly. And so I went first for a month and then I just ended up canceling my return flight, inviting some friends. And that lasted for a while. Most of those are still traveling and most of the people that I got into it are still kind of fully nomadic. Yeah. Highly recommend.
[00:51:22] Ken: Awesome. Definitely shades of Jeff, I think Jeff came out here for a month and they ended up staying six months. I was like, you know what? I respect it.
[00:51:31] Dmitri: That's usually how it goes over in Hawaii. Incredible. That's awesome. Was that your story there as well?
[00:51:44] Ken: No, to be honest, my story is a lot less exciting. I'll spark notes it. So I was in Chicago essentially during the pandemic and I was like dating this girl really? Seriously. She ended things, it felt like out of nowhere. Yeah, I know. It's alright. It was actually one of the best things that ever happened to me, but so I was like, Man, I don't wanna be in Chicago anymore.
We were talking about moving somewhere else. We were like getting ready to make that step. We were, we were living together at the time and then I moved out. I moved in with a friend and I was just like, Man, I don't wanna be here. And coincidentally around the same time my parents bought, A retirement home in Hawaii that they weren't living in.
And I was like, You know what? I'll be a good son. I'll go look after the place while they're gone. And just like, you know, make sure that the waters, the pipes don't freeze, you know, make sure like all the normal stuff happens. So the went, I loved it, enjoyed it. Started looking at places after a couple months and I just got completely priced out because the real estate prices during the pandemic went through the roof just because everyone from San Francisco was like, Oh, I'll just move to Hawaii.
Yep. And it was, yeah, it was pretty absurd. Damn it, Jeff. But but, you know, I'll probably move back to mainland at some point here soon. I'd like to do a year when I just travel the world and work from different locations. Figuring out the logistics of that. I mean, I quite am fond of my girlfriend now and she's working on some ways to be able to be more flexible with where she can work from and those types of things. So if I can organize that, that's something I think it's a hundred percent worth doing.
[00:53:28] Dmitri: If both people are fully mobile, it ends up being quite a lot easier than I think most people think. I feel like a lot of folks ask questions and you don't actually need to know the language because fun fact, you can find people in most places that speak English and you can learn enough things pretty quick to get by, but most places.
[00:53:51] Ken: Great excuse to learn a language too, if you're there for like three months. I mean, that's something I've always wanted to do and I've been putting it off, frankly, so.
[00:54:00] Dmitri: No better way to force yourself than to move to a remote town in Latin America where nobody speaks English. Super fun, Great way to force yourself. Type two.
[00:54:14] Ken: Imagine. Type two. Fun. There we go.
[00:54:17] Dmitri: Do you time where you're gonna go.
[00:54:20] Ken: So I have a bunch of friends in like Thailand now. I think it would be really cool to check there out.
[00:54:27] Dmitri: Like Cheng Mai, that, that seems to be the hub.
[00:54:30] Ken: Yeah, I'd go for, you know, a couple weeks, see if I like it. Put, see if I'd wanna stay longer. Also interested, I mean, Hawaii's pretty great from a tropical perspective. I think Puerto Rico could be interesting just because of tax reasons. And I'm absolutely shameless talking about that.
[00:54:55] Dmitri: I mean, that was crazy. Like you're basically doubling your income, like, not quite, but like multiplying it by like 1.6, 1.7, like if you can pull that off, then.
[00:55:07] Ken: Yeah, I mean, I travel like a ton for work. I was probably on the road for legitimately four and a half months this year. So, you know, it's not that much different from what I was doing, but I'd like to stay in a couple places more consistently rather than like traveling for a couple weeks every month or something like that, that's a lot.
[00:55:26] Dmitri: I did have a friend that like, so I've settled down now in New York for a little bit also after coming here for a month originally and then discovering that, oh, this is great. I think I'm gonna stay here for longer now. But I do have a friend that's still doing the hub thing where she's got like two or three cities that she really likes and she bounces around between them and occasionally goes on trips outside. The benefit is like you develop friend groups, you can keep a bunch of things there. Like you kind of know where everything is at and.
[00:55:59] Ken: That's true. That's very true. Well, we'll definitely talk more about this, you know, probably end of next year is when I'm thinking of kicking things off, and I'll want as many bits of advice as we can get into.
So I have sort of one last question, which again, we've talked off a little bit online, and I, you know, you're a very introspective person. We talked a lot about happiness and we talked about our experiences and finding purpose in some sense. And I'm very interested in your thoughts on the trade off between fulfillment and achievement.
So you've obviously have been successful in your career to date, you've also had a lot of life experience. But how do those things either work together or work in opposition, in your perspective.
[00:56:50] Dmitri: And in your definition, what's the precise definition, a difference between those two?
[00:56:56] Ken: I think fulfillment is like enjoyment of your life. Fulfillment is the sense that I could die and feel like I didn't leave anything. And achievement is however you define it. I think a lot of people, yeah, define it professionally. They define it in terms of usually in terms of income or in terms of a specific goal. But I guess in some circumstances, fulfillment and achievement could be the same thing, but I think that that's probably unrealistic for, for most people.
[00:57:32] Dmitri: Yeah. It's an interesting question, an interesting idea. I think people talk about different values. The one thing that has really stuck with me is like, one unifying principle was live a good story. Like I. I live a good story, live an interesting life. I'm thinking about myself as like a 70 year old grandpa talking to my grandkids about the, like, the moments of my youth and the crazy cool things I did.
And I've had some really, really cool grandpas. And so I've got a pretty high bar to live up to. I think one of them was in the KGB and one of them like ran the nuclear submarine program. And when it comes to the fulfillment, achievement trade off,
there's a bunch of different things that you can do. And certain ones are cheap and certain ones are, are kind of crucial. I think cheap ones include like like go travel, like, like traveling and trying different foods. Like, just going to different places is a remarkably inexpensive way to broaden your life experiences in terms of time and cost.
But then I guess when I, when I think about an aggregate that there was this class that I took in college that said that the things that make you happy aren't things. They're memories. What, what makes you happy at the end is like things that you remember. And so I've tried to do optimize for life experiences and like, like vivid moments in life.
I know, right? Really revelationary stuff for, for millennial, optimizing for crazy who would do that. But at some point, once you've run through all the basic experiences, like you've gone bowling, you've taken a cooking class, you've gone to a few RAVs, you're like, what else is there? And then stuff starts getting more expensive.
You climb a mountain or like you achieve something meaningful in your career. And achieving something meaningful in your career just happens to be something that you're already spending like 40 or 50 hours doing regardless. And it happens to be like one of your biggest sources of influence on your world, like your ability to push and see something happen and your ability to look back and say like, I've done this thing.
And so I think I've done poorly in optimizing for individual happiness, but I think I've done a decent job of optimizing for looking back at having Latin interesting life which is to say I really enjoy optimizing and emphasizing achievement. But the one area where I'll sacrifice that is for opportunities to do something that feels much more memorable and much more fulfilling.
And just like one concrete example, consulting wasn't the highest paid job that I had lined up, but it did give me the opportunity to like, go all around the country and try a bunch of different things. And the, that seemed like, like an obvious trade off that I should take to like broaden my life experiences and broaden my, my memories and things that I can look back on.
So I don't know if that was the, how that fits into the grand scheme of ansys to achievement versus fulfillment, but no,
[01:01:13] Ken: I think it, I think it fit really well into the grand scheme of things. You know, I am also a believer that experiences at a huge premium and we evaluate our lives based on the experiences that we've had, especially with other people.
You know, like, yeah, when we sit in bed and when we're dying, we have probably a highlight reel of the things that we did and. That were memorable and a highlight reel, the things that we wish we did. And I would like the highlight reel of the things that I wish I did to be very short if not nonexistent.
and to me, that's something I think is, is really important. I will also say on the other side of that I read this book a while ago called Story Worthy. And if you haven't read it, I highly recommend it. It's an incredible book. But essentially the guy who wrote it, he wins this, It's like the moth storytelling competition.
He's one at like an unprecedented 20 times. And the best stories aren't the ones that have the most action or the craziest things. They're the ones that have the most meaning. And he does this exercise where every day he writes what his story for the day is. And that's how he like, keeps track of every day of his life.
And if you give every day meaning, if you give every experience meaning or some part of that day, meaning. Then it is part of that highlight reel then, then something special did happen in every day of your life, and that means you do have more memories. So I think to me, that was something that was completely groundbreaking.
It was like, wow, like I don't wanna live any day that I felt like was monotonous. Like something should happen every single day where I felt like there was something special about it or that there was a story around it, or that like, I could tell it, you could probably take any day of your life and something happened.
You could tell it at like a cocktail party and like, you know, get people interested. You, you, you can frame any day of your life like that. And to me that was like, wow. Like why don't I, like every day could be interesting if I made it that way.
[01:03:14] Dmitri: Interesting. You have, you found that, like, thinking about it that way actually makes your days more interesting when you look back on it?
[01:03:20] Ken: Yeah, I mean, there are very few days I have where I was like, well that was a waste. Like, yeah, I did my work. Yeah, whatever. But it was just like, like, You know, like it was wasted. Like I think if you're just sitting at home working and maybe you like made some progress on a project or whatever it is, I feel like that's, in some sense, a wasted day.
Right? Yeah, I feel like something more has the be of it. Like something in in me can change. It doesn't have to be in my work or anything along those lines.
[01:03:50] Dmitri: It, it's one of those things that like, as an empiricist, it completely baffles me, but this idea that like you manifest something in your brain and that seems to like actually abjectly increase the chances of that happening.
Like speaking as somebody that like looks at the world in data points and this is how this works. Saying like, you're gonna think something and then it's gonna happen more. Just makes zero sense. But it does seem to work pretty consistently well.
[01:04:14] Ken: Yeah, I think if you have that philosophy though, you can also make your own stories, right?
So it's like, oh, you know, today was really freaking boring, but at 5:00 PM I was like, Screw this. And I jumped off the bridge into the water. I just like did something totally random. I'll remember that that's something, it's like, you know, why did I do that? Why, why? You know, like in telling someone, like why would you that it's like, oh yeah, you can, you can make up whatever your reasons are, but it was inherently against the monotony that you were working through that day.
And you do have agency over your story that day, Right? So there's kind of two sides of it. It's like you can do something or you can make something of it, which is an interesting paradigm.
[01:04:53] Dmitri: So then getting to the root of this question, I think the hard problem is like, let's say you've got two things in front of you and one of those things is something that like helps you achieve something that excites you about your career.
And one of those things is like deepening a close relationship or going on some adventure that you think is gonna add to your collective story. How do you, how do you trade off between those two?
[01:05:16] Ken: Well, personally, I try to make those. In line with each other. do both. Yeah. Yeah. I mean, I mean, that's how I've hopefully designed my life.
I mean, this podcast, right? I gotta have amazingly interesting conversations with people like yourself. And I also get to turn this into content that other people can see and view. And in theory, I also get, you know, get paid in some sense from sponsors and those types of things to do this. So it's like career alignment like philosophical and interest alignment, and also upside, like manufactured serendipity because other people will see this and it could lead to additional opportunities, additional guests, hopefully it pays dividends for my guests as well, where they get heard and there's value associated with that.
So not everything can be like that. Like, you know, you don't wanna be one of those like family vloggers that's going around and it's like, Oh, we're doing all these cool stuff, but we have to record it the whole time. But I feel like there is a sweet spot where a lot of the things. That you want to do in this world today can be in line with the things that you also want to achieve.
[01:06:22] Dmitri: You can certainly make them closer. Just like figuring out like what's the, what's the overlap between the stuff that pays me dollars and the stuff that brings me joy as like a surprisingly, I don't know, surprisingly underappreciated way of iterating towards that.
[01:06:42] Ken: Yeah. I will say on the flip side, you can have too much where it's like when I was playing professional golf, it was like, I don't like this anymore because my livelihood is dependent on it.
And you can fly too close to the sun. So I don't even know the answer to the, to that where it's like, how far is too far? When does it become not fun?
[01:07:00] Dmitri: It does make for an interesting memory though. You're a professional golfer.
[01:07:04] Ken: Also true. Also true. I am a big time believer in the philosophy of trying to live an interesting story.
And making a memorable life, because to me that's something that, again, we have this agency over and it's all we're gonna be able to have when we're like on our death bed. Right. Maybe you'll have family in whatever it is. That's kind of part of it, right? That's part of an interesting life.
[01:07:35] Dmitri: Yeah. I yeah. Quality of your decisions multiplied by luck equals results and might as well optimize the quality of the decisions.
[01:07:45] Ken: Yeah. And have as much influence on the luck as you can too.
[01:07:48] Dmitri: You should take that idea that we just talked about for two minutes and then expand it into a LinkedIn post and maybe like a nice think book that you can put at like WA airport with a catch title so that other people can be inspired by an idea and then spend six hours reading about it in an audio book.
[01:08:04] Ken: There you go. And it all comes full circle. Well, Dimitri, this was awesome. I super enjoy talking to you. Is there anything you wanted to share about what you're working on? Any final message you have for the the people and you know, if you would like people to be able to get in contact you, what's the best way to do that?
[01:08:25] Dmitri: Sure. If you want to do interesting stuff and you want to be in data science, just build stuff. Literally go on the weekends and build stuff. If you're looking for a job, come apply for an analyst or data scientist role at Opendoor. It's an amazing place to do analytics and data science. I love, I love everybody here.
Check out my project on yourmove.ai and if you wanna get in touch with me I think I'm on Instagram @dmitri_am or just find me on LinkedIn. Yeah.
[01:08:56] Ken: Hell yeah. Thank you so much again.
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