kyle-mckiou-2020-04-06.mp3

Kyle McKiou: [00:00:02] In the long term, the only successful, if you keep growing and improving and actually becoming more because that's how you make a bigger impact, that's that's how you earn more is by becoming more, by learning more skills, by becoming better at your job, but taking on new challenging opportunities that you're afraid of and learning and pushing through that no matter what thing you're practicing, you can get better at it. Right. You can always get better if you have the right mindset of I'm going to try. I'm going to do my best. I'm going to learn from my mistakes.

Harpreet Sahota: [00:00:47] What's up, everyone? Welcome to another episode of the Artists of Data Science. Be sure to follow the show on Instagram at the Artists of Data Science and on Twitter at Artists of Data. I'll be sharing awesome tips and wisdom on data science as well as clips from the show. Join the Free Open Mass, My Slack Channel by going to Bitly dot com forward slash artists of data science. I'll keep you updated on my weekly open office hours. I'll be hosting for the community. I'm your host, Harpreet, so let's ride this beat out into another awesome episode.

Harpreet Sahota: [00:01:30] Our guest today is steadfast in his belief that it really shouldn't be so difficult for hardworking, dedicated people to find a job that they love.

Harpreet Sahota: [00:01:38] In the exciting field of data science, he's taking the lessons learned from the struggles he faced while trying to penetrate the field of data science and packaged them into a course so that up and coming data scientists don't have to go through months or years of the same heartache he did while striving to achieve an exciting career and live the life that they've been dreaming of. He's provided leadership value in rules from lead data scientist to senior director of data science at globally recognized brands such as Anheuser-Busch InBev, Constellation Brands and the marketing store. Throughout his journey, The Corporate World. He's been recognized for being a well-rounded engineer and scientist with experiences spanning applied math, software engineering and technology management. He's known for his remarkable talent for ability, skill, balanced and productive teams, and having an eye for highly motivated data scientists who go on to make significant contributions to their teams. In the last two years, he's focused primarily on cultivating a program that keeps both job seeker and hiring manager perspectives in mind and has been working tirelessly to distill his years of job search knowledge into a battle tested system that has helped hundreds, if not thousands of students transition their skills from an academic environment to the business world. So please help me in welcoming our guest today. A man who is on a mission to fix the broken method of today's education and recruiting systems. Kyle McKiou, Kyle man. Thank you so much for taking time out of your schedule to be here today. Really appreciate you joining me on the podcast.

Kyle McKiou: [00:03:03] Harpreet. What's up, man? I appreciate you having me. What an introduction. That sounded awesome.

Harpreet Sahota: [00:03:10] Hey man you're well-deserving of it, man. You've done so much to help so many people, including me. I can be more honored to have you on my show and pick your brain and maybe help kind of understand how your journey has progressed from academia to software engineering to data science. So talk to us a little bit about how you first heard data science and what drew you to the field.

Kyle McKiou: [00:03:30] Sure. So I originally went to school because I was interested in working at a big hedge fund or a big investment bank or something as a quants.

Kyle McKiou: [00:03:41] So I decided I was gonna get a Ph.D in mathematics. So I made the switch. I was actually studying exercise science at the time. I made the switch. And right into my first semester of college at University of Illinois, I was taking senior level math classes that I took five math classes my first semester. So I was working my way through the mathematics degree. And then after a year or so, I realized that I didn't want to really be part of the banking system. I didn't really see that necessarily adding a lot of value to society. I didn't think that was going be very fulfilling. So I was kind of stuck in this position where I had a mathematics degree. I'm like, man, I don't know what to do with it because I don't want to work in banking. I don't want to be a math teacher. I don't want to work for the NSA. What can you even do with a math degree? So I started looking at what can I do with this? How can I start applying this knowledge? And that's where I started studying economics and computer science and statistics and all these other fields that were related to math. And I got really interested in doing mathematics on computers. So I ended up starting doing a PhD. In scientific computing. And then I decided that a life in academia wasn't where I wanted to go either, because that's just a lot of research and writing papers.

Kyle McKiou: [00:04:50] And you never really get to do anything. You don't really get to make a real impact in the world. You don't really get to apply your knowledge. You just try to find new knowledge for the sake of it. So I ended up dropping out of my P. D program. I was actually doing a computer science PSAT at University of Illinois at the time, and I just left for the master's degree. And so I had a masters degree and then I got a job doing software engineering. They were basically helping companies do electromagnetics simulations. So if you wanted to design a stealth fighter or a battleship, we were creating the software for you to do those simulations and development. Now, this sounds really cool, but the problem is that all of our clients were classified. So it's basically all top secret. And we would roll out this new software, all these improvements, and nobody would say a single word back. It's like if you had clients that just totally ignored you and then you thought, well, what the hell? Do people like the product? Is this helpful? Is this useful? Are they enjoying it? Or is this just a big waste of time? So it's a little bit frustrated that I couldn't really tell if I was making an impact. And that's what I learned about data science. I think I probably saw the same article that said that, you know, everyone else off Harvard Business Review, sexiest job of the 21st century friends sent it to me. And I looked at it as a man. This seems cool because you really get to make a real impact on businesses. You're much closer to the business side, the application side and the work that you do while it's still mathematics and computer science and statistics. And these are things it's actually used by the business to get a real result. It's not.

Kyle McKiou: [00:06:29] Just theory, it's put into practice, and that's how I got into data scientist and man, how do I really make a difference, a positive impact in a company that I can see? And that's why I decided I got to make this transition into data science.

Harpreet Sahota: [00:06:42] What do you love most about the field of data science?

Kyle McKiou: [00:06:46] Yeah, I mean, I think the best thing is, is, like I just said, that you can use your technical skills to make a real business impact.

Kyle McKiou: [00:06:53] There's a lot of jobs in I.T., for example, where, you know, you're doing work, but you don't necessarily see the impact of it. You're doing a lot of support work, whereas data science, you're much more on the front lines. You're much more making an impact and involved with the business side of things. So to me, that's much more exciting and rewarding to see the business accomplish its mission. You know, whether that's selling a product or helping save the world, whatever it is, you get to be a part of them completing that mission and not just the support role where you're making sure that, you know, some algorithms or something works in the background for them.

Harpreet Sahota: [00:07:29] And so you've had some awesome work experience the last few years, holding several high level positions at some awesome companies, taking that experience into consideration. Why do you think the field is headed the next, let's say, two to five years?

Kyle McKiou: [00:07:43] It's it's kind of a funny question, because if you ask me this same question two to five years ago, give me the same answer. And that's because really it's been slow to adapt. So what needs to happen to make data science more scalable is it has to be much more systematic. It has to be much more organized. It has to have much more of an engineering focus and not just an ad hoc analytics focus, because a lot of companies have tried to set up data science practices really quickly. And what they do is they get one person or maybe a couple of people and they say, oh, we've got some data. What do we do with it? How do we make more money with it? And that's just an approach that doesn't work at all. And then it ends up, of course, not working because it's not planned out. There's real no there's no real way to get value from this. There's no way to scale it. There's no way to make it repeatable. And then they say, oh, data science doesn't work for us. And they shut it down or they struggle year after year. So really, it has to be a much more systematic engineering focused discipline because that's what makes it scalable. That's what makes it repeatable. That's what makes it adaptable to new markets, to new problems, to new situations. And when you can do that, that's when it makes a lot of money and a lot of impacts for the company. And that's when data science flourishes, gets a bigger budget, makes a bigger impact. So it's really a focus on engineering, more so than just can we build a machine learning model or do we have, quote, artificial intelligence?

Harpreet Sahota: [00:09:20] Are you an aspiring data scientist struggling to break into the field? Well, then check out DSDJ.co/artists to reserve your spot for a free informational webinar on how you can break into the field. That's going to be filled with amazing tips that are specifically designed to help you land your first job. Check it out. DSDJ.co/artists.

Harpreet Sahota: [00:09:46] Kind of piggybacking off that response to in that vision of the future. What do you think is in the separate the great data scientists from the merely good ones?

Kyle McKiou: [00:09:56] So what will separate, the great ones from the good ones is not their skill in statistics or building statistical models or even software engineering, for that matter. It's really understanding the context of the problem that they're solving. So you can be really good if you make this repeatable, if you understand how to build good models. If you can take data and turn it into predictions. But if you want to be great, you have to understand the context of the data, where it came from, how it was collected. The relevance to the business and the problem that you're trying to solve. So you can build a model that's, you know, much more subtle and specific to the situation of the problem you're solving, because a lot of people just see numbers and they say, oh, well, I want to build a model that has a high R squared or that has a low error. And it's great to have a model with a small error, but that doesn't necessarily make the biggest business impact. So it's realizing that sometimes your data is biased, sometimes your data is not good. And a model that has more accuracy, isn't it? You know, whatever your accuracy metric is, but a model with more efficacy is not necessarily better. In the real world, because, you know, this is a hypothetical, perfect situation model. Whereas the real world is big and it's messy and there's a lot of different angles to it. So understanding the context of the situation is really what takes someone from good degrade and making a moderate impact to a huge impact.

Harpreet Sahota: [00:11:21] Awesome. Yes. Awesome perspective and awesome advice. 100 percent agree with that. So coming from that really strong software engineering background that you have. What are some of the challenges you see a software engineer facing as they try to transition into data science? And do you have any tips on how they could overcome these challenges?

Kyle McKiou: [00:11:38] One thing is that, again, context is is a big challenge and understanding the numbers and the statistics, that it's not always what it seems.

Kyle McKiou: [00:11:49] And just because you can build a model that looks good doesn't mean that it is good. So one thing is simply recognizing your weaknesses and saying, hey, where do you know? Where am I not strong? Where do I need to get help? Where do I need to consult with other people to make sure that I'm understanding this correctly and that we're doing the right things because it's very, very easy to fall into the trap of saying, well, I can download this package and I can build a machine learning model with one line of code and then I'm done. Why do I need a statistician? Why do I need someone who knows more about math or economics for the problem or trying to solve? I just built the fucking whole you know, the whole code with one line. That's it. I'm done. So it's easy to fall into that trap. But you have to realize that just because you can doesn't make it correct. So that's the biggest thing, is just spotting your weaknesses and finding other people who can fill in those gaps and listening to them and making sure to pay attention that you understand the problem. And you're not just building a piece of software that's useless for everybody.

Harpreet Sahota: [00:12:42] That's a great point. The kind of segway into my next question, which I think you pretty much answered with with this response. And that was, you know, what are some challenges that I'm using? Air quotes here, quote unquote, notebook data scientist can face when it comes time to production, NYes, a model. Do you have any tips on how you can overcome those hurdles?

Kyle McKiou: [00:13:00] Yeah, I would be to scrap the whole notebook and start over and build a production system, because the problem is the notebook is a prototype. And it's great to build a prototype because you do a proof of concept and you prove out that, look, this thing looks like it works with this prototype that I've built.

Kyle McKiou: [00:13:19] But the problem is your system is going to break. The problem is this whole thing is not scalable, repeatable, maintainable, automatically tested in all these different things. The whole thing has to go into a production system that could be maintained without anybody touching it at all. And it has to be testable. Every line of code has to be testable. And the way that people typically write notebooks is the first line of code runs and then the second line and then the third line. The fourth line. The fifth line. The sixth line. And you realize that there's exponential complexity in your data store. Essentially, the first line modifies stuff in memory, the second line modifies more stuff, etc cetera. And by the time that you get to the end, if something didn't work correctly, it's extremely difficult to debug because this thing just builds on top of each other. And by building on top of each other, it keeps getting more complex. So which need to do is need to break it down into small chunks. And that way you can test each chunk. Right. You just want to have a few lines of code. You can test that this works correctly. That that works correctly. This works correctly. So you really have to make sure the implement functions and classes and make this whole thing much more organized than broken down into smaller, testable parts. So you can make sure that every part of it is working. Because one thing that I learned studying math on computers is you can get results that look correct and you can get results that are usually correct using an algorithm that is incorrect. Right. You can use an algorithm. Correct. Most of the time under most conditions. But there might be some exceptional condition that causes the whole thing to fail. And if you don't have the correct tests in place, you'll never catch that. So people make the assumption that because it works with this one data set in this one context, there's one situation that it's always going to work and it's simply not true.

Kyle McKiou: [00:15:05] It's almost never true. That's why you have to have extremely thorough testing unit testing and integration testing to make sure that not only in that one notebook context with your prototype, that it works, but also it works for every single thing in production all the time. And you're always testing, testing, testing, testing. Here's one of the big problems. One of the big challenges with the science is if you automated system, if it's wrong, you're putting the company at a huge amount of risk potentially if you make a mistake. It could cost the company millions of dollars or more. So you have to constantly be testing and automate that testing and make sure that you're minimizing risk so that other people in the company can be confident in your results. And you can use them with full automation because that's when you become scalable. And that's when you're able to make a big impact. So make sure to test, test, test, test, test. And just because it works, one, two, three, four, five, six times doesn't mean it'll work. The seventh time it could work a million times and on a million one fail entirely. So you've got to test superimportant because the downside can almost always be bigger than the upside. So be risk adverse. Test everything.

Harpreet Sahota: [00:16:18] Really appreciate you going deep on that. Shifting gears a little bit now. I was wondering if you could talk to us about how you first got introduced to the concept of growth mindset and why it's so important for people to understand, especially those who are in the job search?

Kyle McKiou: [00:16:33] Sure. So I talk about this a lot in our course Data Science Dream Job also on Dream Job Academy, which is a course, that we've created not just for data scientists, but for anyone looking for a job. And one thing that I learned in my career is I've tried to move up.

Kyle McKiou: [00:16:51] I was very ambitious. I wanted to get promoted. I wanted to get the next job, take on more responsibilities, make a bigger positive impact with the company, as I was looking for.

Kyle McKiou: [00:17:00] And I had this one decision point in my career where I had three job offers for manager level roles. One was with one of the major insurance companies. One was with a small consulting company that was just starting a data science practice and one was with a direct competitor to my current company. So I was working at Anheuser-Busch InBev. I was in charge of data science for global sales. So if you needed to use advanced analytics and data science to sell more beer. I was the guy I knew how to do it. You know, I designed all the models for our company on a global scale. So I knew exactly what to do to sell more beer using data science. So this third opportunity I had was literally the exact same job. But in another beer company at the third largest beer company, the U.S. Constellation Brands, they own Corona, Modelo, some other brands. So I thought I had three options. One, go into insurance, which was a good opportunity. But I felt that insurance as an industry, we just moved too slow, had too much red tape. I probably wouldn't fit in culturally there. I just wouldn't be happy. So I've kind of ruled that one out. And then I had a small consulting company and then I had the beer company that was doing the same thing I was already doing.

Kyle McKiou: [00:18:12] So I decided, you know, the the first sure win is to do the same thing I'm already doing and get paid an extra seven or eight thousand dollars. So I took the job. And then when I realized, I realized that I made a mistake. So I started reading this book called The 10 X Rule by Grant Cardone. I actually probably hadn't read a single book since I was in high school at this point because I hated reading. But I just moved to Chicago. I was taking the train to work every day and I literally had nothing to do for two hours per day, just sitting on the train, commuting from the suburbs to downtown Chicago. So I decided I should read some books because people tell me books are good and you read books and it makes you smarter, is going to be successful. You should read more. I said, OK, society is telling me books are good. So let me try an audio book. I opened the audible app and literally the first thing that popped up was this big cover. It said the 10 X rule. And then the sub headline was something like the one thing between the matters between success and failure. The one rule for success or something like that. I'm like, all right, that's cool. I'm interested in the one rule for to be successful, whatever. So I start listening to it and I realize that the author was making a lot of great points. And one thing that he essentially said is, if you have to make a decision, if you're afraid of something, you should move towards the thing that you fear.

Kyle McKiou: [00:19:38] That fear is an indicator, that that's an opportunity for you to grow and improve and do more and get. There and what I realized is that I had this decision or I could move towards the thing I was afraid of, which was starting an entirely new practice at a consulting firm where I was going to be on my own. It was just me. No one had any knowledge around this. I was the guy starting from scratch and I was going to build this up or I could do something that I had zero fear over, which is just huge data science to sell more beer. And I chose the zero fear route, which is also probably the zero growth route. So when I realized, like, it just made sense in my mind. If you're afraid of something, it's probably a much bigger opportunity to grow and improve. And that's what I should have done. So that was kind of the first step where I realized that I need to not just try to make a bigger impact or make more money in my career. I need to actually, in the long term, really successful if you keep growing and improving and actually becoming more because that's how you make a bigger impact. That's that's how you earn more is by becoming more, by learning more skills, by becoming better at your job. By taking on new challenging opportunities that you're afraid of and learning and pushing through that.

Kyle McKiou: [00:21:01] And then later, there is a book that I read was suggested by Tom. Bill, you. He said this is the most important book written in the English language. I'm like, well, shit, that's a pretty strong recommendation. I guess it's better than every other book besides the Bible or something. I don't know. I don't know what the other books were that he was talking about, but this is the best book written in English. I should probably check it out because this dude is pretty freakin successful and I definitely respect him. So I go and read this book Mindset by Carol Dweck. And the whole premise of this book, and it's and it's based on research. So she's a researcher essentially in learning and psychology. And the whole premise is that no matter where you are today and no matter what skill you have, no matter what thing you're practicing, you can get better at it. Right. You can always get better if you have the right mindset of I'm going to try. I'm going to do my best. I'm going to learn from my mistakes. I'm going to keep improving. Every failure is an opportunity to grow and learn. And it's not a judgment of me not being good enough for me not being talented enough. It's simply just an opportunity for me to learn and grow. And then when I realized that, I realized that there are a lot of areas in my life that I felt that I wasn't, that I couldn't improve just because I was naturally not good at something.

Kyle McKiou: [00:22:31] And it might be true that you're naturally not good at something, but you can improve and you can consistently improve over time. And most people that get really, really good at anything are simply the ones that have practiced the most that put the most time in that have been the most dedicated. That started with the most belief that they could improve. And then they put the work in. And every time they failed, every time they struggled, every time they had a roadblock, they looked at it and said, wow, this is amazing. I've just failed here. This is an opportunity for me to learn and do better and practice this thing that I'm not good at. So next time I can do better. And this is one thing we tried to teach people in our job search courses is just because you've been rejected from one job today doesn't mean you're not good enough. It just means that you weren't good enough yet. And now you're getting some feedback about how you can grow and improve and do better next time. And if you do that, because most people won't, if you do that, you're going to be ahead of ninety nine percent of people in the sky's the limit for you. So it was really that first book I read by Grant, calm down and then realizing that I was making the mistake in my life. And then later reading the book Mindset by Carol Dweck as well.

Harpreet Sahota: [00:23:43] Absolutely love it. And I came across a growth mindset through the Data Science Dream Job course, and that changed my complete way of thinking and being so it wouldn't have been exposed to it if it wasn't for the course. And so thank you for putting that out there. Awesome. Love it.

Harpreet Sahota: [00:24:00] And also on the topic of Tom Bilyeu, you know I'm a huge fan of his. So I'm pretty much like you could see my interview style and everything that I pretty much just do my best Tom Biluey impersonation here. I like it, but but as in the blog post of yours from a while back about the engineers mindset, would you mind unpacking that for our audience and the role that it plays for being successful as a data scientist?

Kyle McKiou: [00:24:29] Sure. So really, all this means is not just solving a problem, but solving a problem by creating a system. So that's that's really fundamentally all that it is. So it's kind of, like I said, prototype something and just kind of throw together a solution. But what you've got to do if you want to be successful is you've got an engineer, a system that solves the problem for you, because if you have to leverage your own intelligence to solve a problem, well, you're going to be very limited in the amount of work that you can do. But if you engineer a system that takes your logic, that takes your process and that system solves the problem for you, then you can simply repeat the system over and over again without you being the one actually thinking the system already has the thinking into an intelligence built in your job as a data scientist is to implement systems using machine learning, using statistics, using computer science techniques to solve business problems that make a positive monetary impact on a company, or if their goal isn't to make more money, you know, whatever their mission is, your goal is to help them achieve that mission by creating these systems that leverage stats, MLM computer science.

Kyle McKiou: [00:25:47] So that's what it's all about.

Kyle McKiou: [00:25:49] And another way that you can think about it as well is to think that a lot of people approach problem solving, like it needs to be creative, like there's a problem and they need to think up a solution that they need to be really smart and creative and come up with an original idea and be real innovative and awesome. That's like a hard thing to do. And that sounds super scary. But what you really just need to do is break the problem down and make it simple to solve. So you start with, OK, what outcome do I want to achieve? How do I make sure that's objective and that I'm able to measure it? And then you say, well, if this is the outcome I want to achieve, this is the thing that I want to impact some metric or some KPI for the company. OK. What possible actions can we take to impact that? So as a company, can we sell more of a product? Can we do something differently? What levers do you have to pull that could possibly impact the KPI as an organization? Right. And then you say, OK, what data do we have that would actually inform us of how to pull these levers, which levers to pull, et cetera, et cetera? And you start working backwards to the initial conditions and then you figure out what you should do.

Kyle McKiou: [00:27:05] So I know that that would take me 30 minutes to walk through a longer example. But the whole point is you start with the problem you want to solve. You break it down to simpler problems. You break those problems down to simpler problems. You break those down to simpler problems all the way back until you get to your present state and then you see the exact path forward at any point time, you simply stay for this to be true. What would have to be true before this? And you reverse engineer the solution, starting with the outcome that you want, and that's how you're always going to solve a problem. It's not by being a more creative and just hoping that through the ether, the solution is going to jump in your head. You just break the problem down into simpler problems. And I think that's another fundamental part of thinking, like an engineer.

Harpreet Sahota: [00:27:52] Awesome. I love it. Yeah. And you go into you go into this in great depth in the course Data Science Dream Jov, as well as the mentoring calls that you've done in that program. So it's really fascinating the way you deconstruct the problem and break it down. I found it to be tremendously useful in my career as a data scientist as well. So we've talked a lot about like all the technical skills and everything. And I think most up and coming data scientists state they tend to focus primarily on these hard technical skills and they think that that's what's going to separate them from the rest of the crowd. What are some soft skills that candidates are missing that are really going to separate them from their competition?

Kyle McKiou: [00:28:30] Yeah, so I mean, I think in most careers it's not going to be the hard skills that separate you, particularly in data science, where it's it's really as simple as you pick up a book and you read it and you worked on the exercise. And then, you know, statistics like, it's not that mind boggling. There's no there's no special program where you're gonna learn something amazing about the skills like statistics. It's just math. Math is based on a set of rules. There's no opinions in it. You simply learn the facts. You learn how it works.

Kyle McKiou: [00:29:02] And that's what really sets you apart is, like you said, those soft skills, because you realize that if you want to make an impact in the company as a scientist, you're going to need other people to work with you, to cooperate with you, to have a bunch of you moving in the same direction at the same time to try to solve the same problem. So one of the most important things is your communications skills and be able to present your ideas. So first off, you've got to make sure you understand what other people want. Hear that you align yourself with their interests, because a lot of people that I see, they just think about themselves and they say, well, what's fun for me? What would I like to do? What do I find exciting? I like deep learning. I want a job doing deep learning. Deep learning is so fun. And all these crazy neural nets is just so exciting. Need that. Nobody cares. And you're wasting everybody's time and you should get fired because that's that's how business works. Business pay like a business pays you as an investment. You are supposed to generate more money than they pay you. That's the entire concept of capitalism and commerce.

Kyle McKiou: [00:30:13] So you are an investment when a company hires you. They pay you ten dollars and they expect 50 dollars back. So you have to make sure that you are lining your interests with what the company needs, and not what you find interesting or exciting. Because frankly, the company doesn't care. Like you can go home at five o'clock and play with neural nets all you want, but we're paying you to solve this problem right here, right now, under these conditions, with these restrictions. And that's probably not the best solution for us right now. So realize that ONE it's about understanding what other people want and what other people need to be successful. And then TWO formulate the solution that's going to help them. And THREE, you communicate to them, in their words, why and how it's going to help them, because you need their buy-in. Even if you come up with the best solution in the world, it's literally worthless if nobody uses it. So you can you can make a great model that's better than any model that would increase revenue for a company by 50 percent. And that's amazing. That would be the best thing ever. But if nobody uses it, it's worth exactly zero dollars.

Kyle McKiou: [00:31:27] So you've got to make sure that you're communicating to people in their words and showing them how and why your work is going to help them achieve their goals. And when you do that, that's when they're going to start to have faith in you. That's when they're going to give you backing. That's when they're going to help push your model and your work into production to actually make money for the company. And then once you get that positive, the result, of course, they're going to double down and make a bigger investment with you and your team. So it's really about understanding other people, understanding the context of the problem and creating a solution that fits within the constraints that you have. And then communicating it to people in a way that they see the value in it, because you seeing the value in it is not good enough. The other people have to see the value in it. Otherwise, it doesn't have any value. So I think it's a big disconnect for people of good versus useful. You can make a good model, but it's not useful if it's not in production. So that's the key - It's communication.

Harpreet Sahota: [00:32:35] I like that man. And so your job as a data scientist is not to be a walking encyclopedia of just every algorithm and all its different configurations.

Harpreet Sahota: [00:32:44] Your job is to  know when to apply something, how to apply it so that it works for the company to solve their problems. What would you say is the single biggest myth about breaking into data science? And can you debunk that myth for us?

Kyle McKiou: [00:33:01] Sure. I mean, it's it's easily that people think they need the most technical skills. And that's a myth because most of what you need to know, you'll learn on the job anyway. So what companies need, they need two things. One, that they need you to have some sort of skill that's going to allow you to contribute in the short term and do something useful that helps somewhere. And two, you've got to show them that you're the person that constantly learns and improves over time because you're the person that constantly learns and improves over time. Then eventually you're going to be a much better investment that someone who's more skilled right now but has a fixed mindset who doesn't keep improving. So people think they need to know everything right at the start. They need to have all of these technical skills. And really, when you think about it logically, you only need enough technical skills to pass the interviews. So that's it. You look at what questions will be asked during the interviews. What will I need to know to pass the interviews? That's all you need to know.

Kyle McKiou: [00:34:02] You don't need to know everything to be successful in the job. You simply need to be able to learn it on the job. So people need to stop trying to master everything because then they spend years and years and years learning skills. And then when they get hired, they realize they have no idea what they're doing anyway because they never applied those skills in this context. So really, you just need enough skills to get hired and then the ability to learn and adapt and improve on the job. Because honestly, no matter what you've done in the past, it never guarantees your success in the future. Any job you get hired for, it's always gonna be a new situation, new problems, new people, new data, new implementation, new market, new everything. So it doesn't matter how much you know, it matters how much you can learn and adapt. So just enough skills to get the job and then learn and adapt on the job instead of trying to learn everything all at once before you get started.

Harpreet Sahota: [00:35:00] Awesome. This is amazing advice, and I think that's something that everybody who's listening that is aspiring as a data scientist who's making that transition needs to understand. So thank you for sharing that. Not many people know this about you, but used to play professional poker. So what lessons from those days have stuck with you? And how is that helped you in your career as a data scientist?

Kyle McKiou: [00:35:23] Hey, it's funny. It's so so that was actually playing poker. One thing that I learned in that time is that that information is the most valuable commodity in any sort of competitive environment.

Kyle McKiou: [00:35:40] So I think that definitely shaped me moving into data science later, because I realized that if you knew more than your competitors, you could always make a better decision. Now, there's always some uncertainty or some risk associated with your decisions. You're never 100 percent sure, even if you make the right decision. It might not work out.

Kyle McKiou: [00:36:00] That's simply what the real world is like. But if you have more information than everyone else, you can tend to make better decisions and over time you'll get better results. And that's one thing that I saw this powerful with data science is admen. All we're doing is we're trying to take as much data as possible, turn that data into actionable information and knowledge so that this business can make better decisions on average over time, thus beating its competitors or not even looking at the competition, really just serving your customers better.

Kyle McKiou: [00:36:35] And you'll end up getting a better result. So that was the biggest thing to me, is the more you knew, like in poker, essentially what it means is you need to look at your competitors and find their tendencies. You need to study them, study their hand histories. And if you can use software to do that, playing online, of course you want to do that as well. But if you can understand other people's tendencies and what they're going to do in different situations, then you're able to make a better decision about other people and you'll end up getting better results. So it's the same thing in business with data science as well.

Harpreet Sahota: [00:37:07] Last question before we jump into our lightning round here. What's the one thing you want people to learn from your story?

Kyle McKiou: [00:37:12] I mean, the one thing that people should learn. Well, I'll just tell you is that the one difference between people that are, quote, successful versus not successful is they've done the thing. They've done the work. So if you want some sort of result, it really just comes down to you putting the work in to get that results. And then you being patient enough to not quit. That's all there is to it. You put the work and you keep improving and you don't quit. And if you do that, you'll get there. I guarantee it. I've seen it over and over again in my own life, in my own career, with my business, with other people that are more successful than me. That's really just the one thing. It's you put the work and you just do the thing. I think there's a quote from Henry David Thoreau. He said, Do the thing and you will have the power. Could be slightly misquoting him. But, man, I found that super powerful and just struck me as being so true. If you do the thing, you will have the power. And that's what it comes down to. It's not whining or complaining or saying that things are unfair, things aren't possible. You just do the thing and you will have the power and you just keep doing it. You keep improving and eventually you will get there.

Harpreet Sahota: [00:38:27] I love it. Instead of being stunned into inaction by the seemingly massive problem, the seemingly massive difficulty of this thing you're facing. Just fucking do it. And then chip away at it and get it done. And that's what we'll take you to the next level.

Harpreet Sahota: [00:38:47] What's up, artists? Be sure to join the free, open mass. Much slack community. I'm going to bitly.com/artistsofdatascience. It's a great environment for us to talk all things data science, to learn together, to grow together. And I'll also keep you updated on the open biweekly office hours I'll be hosting for our community. Check out the show on Instagram @theartistsofdatascience. Follow us on Twitter @artistsofdata. Look forward to seeing you all there.

Harpreet Sahota: [00:39:17] I dig it, man. So let's jump into our lightning round here. Just just quick answers to these questions. What is your data science superpower?

Kyle McKiou: [00:39:23] Well, I don't really have one anymore, but it would be framing business problems and understanding what problem we need to solve.

Harpreet Sahota: [00:39:30] All right. If you if you had a magic phone that let you contact 20 year old Kyle, what would you say to him? 

Kyle McKiou: [00:39:39] That's a good question. I'm slowing down your lightning round here, man.

Harpreet Sahota: [00:39:42] It's all good. I'll Edit edited out the blanks.

Kyle McKiou: [00:39:45] I guess I guess it would be that you can do anything you want. You just have to do it. And you, no matter what anyone else tells you, they're wrong. They don't know. You can do anything you want. You just have to do it and not quit.

Harpreet Sahota: [00:40:02] So we said we talked about the 10 X rule. We talked about mindset. Barring those two, what would be the number one book you'd recommend for our audience to read? And what was your most impactful takeaway from it?

Kyle McKiou: [00:40:16] What that's really good is Psychocybernetics. And really that book talks about the concept of self image a lot.

Kyle McKiou: [00:40:24] Is that how you think about yourself and what you believe to be true about yourself will influence your actions and of course, your actions will influence your results. See if you can start to think about yourself in a different way. If you can start to think about yourself and the way that you want to become and start acting that way, it'll eventually become true. So definitely recommend checking out Psychocybernetics and another good one, too Deep Work by Cal Newport. I think that that's super helpful for a lot of people that really just realizing that focus has the lack of distractions and you can set yourself up to be more productive by removing distractions in your life. That's a good read, too.

Harpreet Sahota: [00:41:05] Awesome. Awesome. So what's your favorite poker hand?

Kyle McKiou: [00:41:10] I don't have one man, like whatever wins. Like it doesn't it doesn't matter. Everything's relative. You just have to have a hand that's better than the next person who goes to showdown with you.

Harpreet Sahota: [00:41:22] You need to have a hand that's better? Or you just need to play it better?

Kyle McKiou: [00:41:25] Well, if you need to if it goes to show down, you need to have a better hand. But if not, you know, you need to play it better. So whatever is good enough to win.

Harpreet Sahota: [00:41:35] Awesome. So what's the best advice you've ever received?

Kyle McKiou: [00:41:43] Man, I can't think of a lot of. Good advice. Honestly, I never really had anyone to mentor me or tell me any good advice and helped me along the way like I just had to teach myself. So maybe it would be that you should listen to. You should find someone who knows more than you and has had some of the success that you're looking for. And take their advice. So if I could turn it around and say one piece of advice for other people, it would be that because I didn't really have that opportunity, for whatever reason, that I just kind of had to teach myself a lot of stuff. So, man, if I had if I could have had someone else to teach me, that would been awesome. So listen to other people that have been where you want to go. And they'll help you get there more quickly.

Harpreet Sahota: [00:42:34] What motivates you?

Kyle McKiou: [00:42:36] You know, I just want to I think a lot of people are holding themselves back. And I just want to see people achieve their potential. I really wish that again, that there is someone to help me. And if I could just go out and help other people achieve their potential and do what they're capable of and make a positive impact in the world, I think that not only would they feel good about it, and of course, that would be great, that I would feel good. They're successful, but would help the world in general be more productive, be happier, be more prosperous. So I can help people reach their potential. More and more. That would really make me happy.

Harpreet Sahota: [00:43:15] Awesome man. So how can these people connect with you? Where can they find you?

Kyle McKiou: [00:43:20] Yeah, it can follow me on LinkedIn -  Kyle McKiou. So I'm sure you can just type my name in there and I'll show up also on Instagram and YouTube, YouTube, both data science, dream job and Dream Job Academy.

Harpreet Sahota: [00:43:38] Awesome man. Well, thank you so much for taking time out of your schedule to talk with us today. I know there's so much that a lot of our audiences can take away from this conversation. So thank you so much. Appreciate having you on the show.

Kyle McKiou: [00:43:50] Yeah, for sure. It's my pleasure.