Why Hiring And Retaining Top AI Talent Has Become Harder Than Ever

In this episode of Data Analytics Chat, we welcome Misha Trubskyy, head of Claims Data Science at Mercury Insurance. Misha shares his journey from academia to corporate life, highlighting his transition from econometrics to leading data science initiatives in insurance. He discusses the significance of continuous learning, the challenges of AI and data implementation, and the importance of hiring and retaining top talent. Misha also delves into his leadership principles, the value of technical proficiency, and the importance of empathy in managing teams. Key issues such as the evolving hiring landscape, market conditions, and strategies for organizational growth are explored in-depth.

00:00 Introduction and Personal Philosophy
00:14 The Importance of Continuous Learning
00:44 Challenges in the AI and Data Science Field
01:20 Guest Introduction: Misha Ky
02:24 Misha's Career Journey
03:43 Current Excitements in AI and Data Science
06:09 Navigating Human Elements in Claims
07:37 Leadership Challenges and Lessons
11:28 The Value of Individuality in Leadership
19:31 Advice for Aspiring Data Leaders
29:52 Hiring Challenges in AI and Data Science
37:02 Finding the Right People for the Job
37:23 The Importance of Critical Thinking
37:41 Authenticity in Interviews
38:26 The Role of Technology in Interviews
38:51 Evaluating Candidates Beyond Technical Skills
41:36 The Misconception About Technical Skills
42:25 Personality and Attitude in Hiring
44:21 Challenges in the Job Market
48:55 Investing in Junior Staff
01:04:33 Retention Strategies for Top Performers
01:10:03 Future of the Hiring Landscape
01:13:42 Conclusion and Final Thoughts

Thank you for listening!

Transcript

misha trubskyy: 0:00

In general, I'm a kind of do first ask for forgiveness later type person. Still am. And that's, and that's something that's not necessarily the best feature that to have in a corporate right environment. You cannot really as assume that you are done learning ever anymore. And if you see a person that refuses to learn, that's probably a, the largest red flag that you can have, that you never want to work with a person like that because that's a dead weight pretty quick. LLM is not gonna solve everything, but also sometimes the problem is actually solved. By taking an average, right? You don't even need a model. You don't need anything complicated. You just need to understand the problem. One of the problems I think that LLS and AI, modern AI will bring is that people are ignoring junior stuff, right? There's a lot of you, you read that, that say, oh, the simple tasks are going to be outsourced. So why do we need to hire junior staff? And it'll backfire in the future, right? Why? Because where are you gonna get expertise?

ben parker: 1:20

Welcome to Data Analytics Chat, the show where the wells leading data and AI professionals shared decisions, lessons and breakthroughs that shape their careers. Today I'm joined by Misha Ky, head of Claims Data Science at Mercury Insurance, a leader driving innovation. AI across the insurance space. In this episode, we'll unpack defining moments that shaped me's career, the challenges he's faced, and leadership principles that have guide guided his success. Then we'll look into the data topic, which I guess most state leaders are wrestling with right now, is why hiring and retaining top AI talent has become harder than ever and what the best companies are doing differently. Misha, it's great to have you here.

misha trubskyy: 2:10

It is great to be here. Thanks, Ben. Thank you.

ben parker: 2:12

So let's dive in Misha before, because we dive in actually. Could you give the listeners a quick introduction to who you are and the work you are leading today?

misha trubskyy: 2:22

Yeah, absolutely. Thanks for having me. I'm originally from Ukraine. Came to the United States as a graduate student here in Boston at Northeastern University. And and stayed all of my career spent in analytics. Started as a econometrician that's, the regional data scientist, so to speak. Spent about 14 years in consulting environment, in, in academia, doing research. And then switched over to corporate starting at Liberty Mutual Insurance for about 10 years doing both claims and product. Data science and advanced analytics. And for the last year and a half or so, have been with Mercury Insurance, building up their practice of claims data science. Started with a small team building up the team starting to build up the models deploy models for claims, automation claims, triage severity calculations and so on, trying to build the successful claims organization that is data driven, that is digitally driven and knows how to make decisions based on data insights. So yeah. So that's short story of Misha. Yeah.

ben parker: 3:43

So what, I guess what excites you at the moment is a lot, obviously a lot happening in the field. What's exciting you seeing today?

misha trubskyy: 3:50

Certainly a lot of happening in the field, and I think it's been an interesting time overall. I think when you think about my career from beginning to end, it's it's interesting to be in a field that kind of blossomed and grew into strengths right now. 20 years ago it wasn't as sexy for the lack of better word, and it's it wasn't, not as much in demand. Right now what's exciting is obviously this introduction of AI and the alleged ease that it should bring to some of the work. We. I am admittedly on a, I'm not skeptic of the technology, but I am, I'm careful with how this technology is getting implemented and how we should think about how we should think about this technology. So it's interesting to navigate that kind of enthusiasm and fed culture that we see around us. At the same time, trying to actually solve real business problems, deal with dirty data deal with stakeholders, educate stakeholders in how to actually use. The product that team like mine would produce and makes it interesting over overall. And technology is helping greatly. Cer certainly makes some of the stuff that we're doing easier, most, most streamlined, but there's still a lot of challenges in how to utilize this technology, how to do basic stuff still, how to fill the gaps. That organizations have even before they can fully get full benefit from the current innovations that we're seeing around us. So this, all of this makes it interesting times to be where we are.

ben parker: 5:38

Definitely, and I think it's like the business and tech have come together, so there's always gonna have that. The stakeholder challenges there, isn't it? You've gotta educate everyone. Also everyone's gotta learn like this new massive changes that's happening across the business. So it is. It's unwinding all that isn't it, and mixing it into obviously create impact and get the ROI that businesses need.'cause end day look, we still need to focus on business principles, growth, saving efficiencies, that sort of thing, don't we?

misha trubskyy: 6:08

Yeah, exactly. Yeah. And specifically, the claims game is a really interesting one because there's a lot of human component into this, right? We're not making kind of decisions based just on, on data. There's a human on the, there are two humans on each side of every claim. Right? You have insurance company that is representeded by an adjuster that needs to make decisions, that needs to be fair. But also loss conscious and cost conscious, right? And you have a person on the other side that probably not having fun time being a claimant, right? It's a, to a lot of people, that's the worst thing that ever happened to them. So how do you can't put, in, in many ways. You can't put a person that is injured, for example, in, in front of ai, right? That's not the right thing to do from insurance company standpoint. So how do you navigate it? How do you choose the right technology? How do you choose the right way to to navigate the, individual claim, but also how do you choose to navigate thousands of claims at the same time that come through your company? Kind of creates a very interesting mix of problems that sometimes compete with each other. Sometimes you have to pick and choose what you do. And I think. Specifically claims. I'm actually glad that I'm in claims of all other places right now because I think this makes it more interesting overall to be to do what what we're trying to do.

ben parker: 7:36

Okay. Fascinating. So what, I guess when you look back in at your career, sorry, what's, has there been a moment or decision that sort of changed everything for you?

misha trubskyy: 7:46

Yeah. I think like I said, my career split in two parts. One is is focused more around consulting in very highly academic environment. I was a part of consulting research outfit inside the university. Then I switched over to, to, to corporate and that's, that certainly changed how I had to operate, how I had to think. And a lot of challenges, in a, in an environment that there was, before it was a small group. We were focused on kind of public policy issues using, trying to use this state of the art econometrics at the time, trying to do models. The level of expertise was extremely high in terms of knowledge of the subject matter that we were working through. And that, in corporate environment it's a different, it's a different environment. You're not fully independent. You can't really risk as much often. You you you have to follow certain politics and internal environment and procedures. So that was certainly something that I had to learn in the first year and a half was somewhat of a challenge for me. In general, I'm a kind of do first ask for forgiveness later type person. Still am. And that's, and that's something that's not necessarily the best feature that to have in a corporate right environment. And you have to learn how to collaborate a little bit more. With people across, across across the company, but also across the spectrum of competencies. So that was somewhat challenging and something that I admittedly still probably learning how to deal with those issues. Even after, what is it? 13 for 14 years doing this. Yeah.

ben parker: 9:35

I bet. And then so did you have doubts during that period?

misha trubskyy: 9:39

No I think I had, I, I did have some other experience doing in working in in, in corporate environments some with back in Ukraine. I didn't have doubts. I knew that I wanted to take that leap and get out of kind of more academic place into more corporate place. It just was surprising. It just a, it was a little bit different from what I expected at the time. It took some learning but certainly it was the right move for me. Never doubted never doubted that. And and enjoyed working, enjoyed working. And I think some of my skillset and independence really paid off. I was able to make a mark. By going rogue multiple times and creating kind of analytical products that people, not necessarily were asking to do, but just using expertise and experience from from consulting, from from academia helped building those products. So yeah, so it's certainly no doubt, just a challenge that I had to overcome for some time.

ben parker: 10:38

So did you get support through this stage or was it more yourself?

misha trubskyy: 10:44

I was lucky. Yeah, I was pretty lucky. I got into a group that had similar experience to mine before I, I was working with multiple people who. Who were similar educated to me, they knew, understood my background, I understood their background. They also spent some time in, in, in consulting. And yeah, so it was a nice, I got lucky. We had a pretty tight group that helped each other to navigate this environment and we we ultimately we're all successful within, within, within the company. Yeah. It's a, it's a luck to some degree but also really good people around you.

ben parker: 11:24

The power of humans.

misha trubskyy: 11:26

Yeah. Yeah.

ben parker: 11:28

Every leader has a battle story. What was the toughest challenge you faced and what did it teach you about leadership?

misha trubskyy: 11:36

Yeah. I think I think what you what you have what was interesting and very very unexpected for me is the fact that the people were expected to be fairly similar, very homogeneous, right there, there were rules within the company that you have to follow and you've evaluated on a very strict matrix of things. He here's what you need to know in terms of your technical proficiency. Here is how you need to communicate with your stakeholders. Here's what you need to do to develop yourself. And I think that was. Missing on individuality of one particular person. Somebody's extroverted, somebody's introverted, somebody learns differently or communicates differently. And and I found that to be challenging when it was applied to myself that I was put into boxes quite heavily. And also as a, as I became a leader of people and manage people try how to navigate this this my desire not to do that, not to put people in boxes, and at the same time trying to navigate corporate environment where the boxing was pretty prevalent and almost demanded. And I've had a number of people that I either hired or inherited from all the teams that. Come to me as a poor performance per poor performers. And you realize that they're not actually poor performers. It's just the management is asking them to do things that they're not it's not that they're not good at it, but it, the way to do it is not, is not the way to ask them to do it. For example, some people want more independence. Some people like to just take a prob, take a problem and go away and come back in two weeks with a solution, right? So all sorts of issues like that you face. And and that was a little bit interesting kind of to, to learn again, how to navigate that, how to navigate it for myself and how to navigate it for other people and in general, my my my approach to managing and leading people. First of all I try not to manage anybody. That's one of the things that I try not to do. There's a difference between. Manager and a leader, and I certainly try to be a leader, is try to find what ticks with every person. Try to be as individual as possible with every person that, that, that works for me or works for my team so that they can shine within their own with their own strengths. And that's been it has been a kind of consistent challenge for me, I would say, but also I think it's what makes successful teams and that's what makes me lead, being a leader that on average, gets good reviews and good good feedback from people that I work with. So that's, that's been a interesting, I would say that's probably the challenge that I. That I, as a, from leadership standpoint, thinking through constantly and trying to work through,

ben parker: 14:48

So interview, did you learn this the hard way then?

misha trubskyy: 14:51

I learned the hard way in a sense that, yeah, I came in without understanding that's something that's, and I learned it as it was applied to me when I literally, sometimes I wouldn't understand why I had to comply with certain things or behave in a certain way or wait for some decisions to be made versus just go and do stuff. So that was that was something that I had to learn for myself first. And then as I became more comfortable with that, understood why these requirements are in place. I was able to, as I took on leader leadership positions being more flexible in those requirements with my people and help them navigate that and help them shine in their own way within my team. So yeah. So in a sense that's, that was the challenge.

ben parker: 15:43

Yeah. No, I like it. You mentioned you've gotta adapt your approach to each individual because Yeah, every person is. Has a different need and everyone's got different background, different experiences, lots of work in different ways. So I think as a good leader, you need to have that. What's gonna work best for this individual to get the best out of'em?'cause that's what you want outta your team. Don't you wanna get the best out their performance?'cause they'll

misha trubskyy: 16:07

Yeah, exactly. Yeah. Yeah. Exactly. And you need to understand, some people, and you have to understand why people come to work, right? We tend to default into this work for this, for the, sake of the company or for the sake of shareholders, right? Or but the reality is that a lot of people come to work for fulfillment, right? A lot of people come to work to learn, to communicate right? Not necessarily to quote unquote bring value as a main driver of people, right? So you have to take that, you have to take the why you are here. Still channel it so that you bring value and you make the company grow and you make the you help people within the company help leadership within the company to accomplish goals that they have. And that takes that takes a little bit of getting to know somebody, getting to know what the, what, why they are doing what they're doing. And that. I think I find it in interesting, right? I find it interesting. I find it you, I find it better than just pushing people to do things right? Oftentimes you have, here's the project, do it. Why am I doing it? What's the point? How is it fitting into the larger picture? What is it for me? And you have to be able to answer those questions to, to the people that work for you or with you. And when you can do it well you'll be more successful, in my view than if you could just make people do stuff.

ben parker: 17:34

And it also is about the individual being self-aware of what they actually want as well. Because I think obviously there's so many different companies out there nowadays. You could, you need to find one that's gonna, like you said, have that. Why do you wanna come to this? Why do you wanna work for this company like yourself? You've got strong background in insurance obviously, so that's must be of interest to yourself. So obviously if you've got something you are interested in, you are gonna be putting a lot more interest and effort into the work because it's just interest in us keeping you, your brain active.

misha trubskyy: 18:06

Yeah. Yeah, exactly. And I, and what I find is that a lot of people are driven by by interest, right? By learning. So it's not that you have people in insurance that. Maybe maybe for them it's not about insurance, right? Maybe for them it's about how do I use data in my world? How do I use data? How do I use the data that I have in front of me to answer certain questions? Is it about insurance per se? Is it about, insurance pricing? Or maybe it's not necessarily that's what the interest of interest is, right? People could go to work for other companies, not insurance companies, and be interested in the similar tasks. So it's important to know what is it that drives people? And again, in my experience, learning, learning is a big driver. A lot of people in, in. Learning is a must these days, right? You cannot really as assume that you are done learning ever anymore. And if you see a person that refuses to learn, that's probably a, the largest red flag that you can have, that you never want to work with a person like that because that's a dead weight pretty quick. But a lot of people, yeah, driven by learning, driven by personal challenges versus kind of company challenges. And then you channel that into company becoming more successful. And I think that's the approach, that's that, that to me, that proves to be successful over and over again.

ben parker: 19:29

Yeah. No, I completely agree. So what mindset or skills have helped you grow into leadership?

misha trubskyy: 19:39

I think so I would say and again I'm speaking for, for the field that I am in, right? Not maybe in general, but I think probably applies to, to general as well. I think in today's world, there are two, two or three things that that I think helping would help any, anybody in leadership position, especially in data science or AI or, whatever we want to call it these days. One of the technical proficiency I do place a lot of value on technical proficiency, and it doesn't mean that you have to be able to do everything, the spectrum of technical skills these days extremely wide from fine tuned, programming skills to data skills, but you have to be. You have to understand a lot of it, and you have to be able to be really good at some of it. And I think for me personally, the fact that I spent again, beginning of my career in academic environment where technical skill in data analytics, in statistics, in econometrics was developed at a pretty high level. That keeps helping me a lot. I can e even if I don't know something, I can take on a relatively technical paper and understand and be and be a conversational in a subject. So that's for me important. Hands-on experience comes out of that, right? You do see people that are educated well and have a lot of technical expertise, but not necessarily have done the work. At least, again, at least in, in some aspects of the work, right? I can't imagine anybody being proficient at all aspects these days. It's just simply impossible. But then it becomes, so those are the hard skills, so to speak, right? And then becomes what's, what is probably arguably becoming more important communication, critical thinking empathy to the large degree, right? How to be empathetic about people's people's problems. How to help people learn. How to help people navigate. The environment that they're in, being a mentor. Those are softer skills that one need to, one, need to develop and to be successful leader. And those that I'm trying to, some of them are easier for me, some of them are harder for me to maintain, but those are that I'm trying to develop and exercise. It's important to exercise those train, practice how to do these things well over time consistently.

ben parker: 22:06

Yeah, I agree. It's constant learning in that respect.

misha trubskyy: 22:09

Yeah.

ben parker: 22:09

And I guess how did you get go about lead learning in sort of them leadership qualities, like the softer skills? Was it through like peers? Was it mentoring, training? How did you do it?

misha trubskyy: 22:22

I never had, yeah. Yeah. I never had formal training in leadership peers. I was lucky over my career to have great mentors and great great people that I worked with. And you learn, you learn, I think you learn things that are not obvious. You learn that it's okay not to know something or you learn that it's okay to be wrong or sometimes and, being okay with it. So those are skills that probably harder for, especially for younger generation to to accept. And you observe people you pick and choose what works for you. You read yeah. I probably don't, can't say I had any sort of formal training like that but mentorship and working with great people and talking to to, to people and having friends around you who are in the same kind of position that would help me. And that's how I I learned some of the softer skills and kind of leadership ideas that I have.

ben parker: 23:21

I think it's at the time when you're working with great people, you don't realize that they're gonna impact. To your future, in my opinion, because I think you look back now and you think actually him or her, whoever you worked with, had that quality and then you took take that and then blend it into your skill, don't you?

misha trubskyy: 23:40

Yeah. Yeah, definitely. And and yeah, the maturity comes later, right? And you always think to yourself, oh, I wish I knew that 10 years ago, 15 years

ben parker: 23:48

Yeah.

misha trubskyy: 23:49

But certain, yeah, but certainly, yeah, certainly you learn things and maybe you don't understand them at the time but you absorb and and yeah. And at some point it comes out. Yeah. So yeah, great people is the key. And it's a, it's luck. A lot of times it's luck, right? You, when people look for jobs, I think especially those who can pick and choose, that's one of the things that you think about before you get hired. Make sure that you do a lot of due diligence, right? And that you pick team. That compliments you, that you're surrounded by people that are that great and help you and you can help them, versus hoping for the best. That, that never works out. I don't think that works out well for anybody.

ben parker: 24:36

Yeah, definitely. It's like the non-monetary benefits.'cause

misha trubskyy: 24:41

Yeah,

ben parker: 24:41

obviously if you work for a great leader, lot. That's gonna impact your future lies. That's just oly. People need obviously certain money, et cetera, to live, et cetera. But also you need to do look at the external benefits, like who the team, what's the work you're doing as well, that sort of thing. There's a lot that goes into it. Which I think people do overlook a lot of the time. Like you always, in my opinion, you should meet team members.'cause if you're working with a great team like we've discussed, you are gonna, it's just gonna benefit your career down the line.

misha trubskyy: 25:11

Yeah. Yeah. I certainly advise people to. When they get hired sometimes it's unfortunately maybe not possible. Sometimes people especially in a tougher markets rec like we have right now, sometimes, but people, need to, need a job. But if you have that opportunity to ask even after you already signed the contract, meet the team try to get to learn what people are interested in, what drives them. And it applies to both people who in, in managerial positions that start in, that inherit the team, right? That's probably the most challenging environment for any leader is to come in a team that's already in place. And trying to take over and learn about people. I, in my current position, I was, it was a little bit easier for me because. I was I was lucky to be able to hire most of the people on the team. So that was you look for people that fit in your understanding and your culture and and trying to get, good people that that you feel to will be able to work together well. But you, if you inherit the team that's obviously much more challenging because you don't have the choice and you have to adapt and learn and in insert your value into the team. And make sure that everybody benefits. It's the worst thing that's a new manager comes in and there's no value that they can bring other than, manage essentially. There's no leadership that, that they can actually bring. But so they end up just managing you, and then it's end up micromanaging you. And that's the worst quality in a, in any person in the leadership position that you can have. Yeah. Yeah. Did I lose you? Sorry.

ben parker: 26:57

I'll do that again. I can, we can edit. I think someone tried calling me. Okay. If you could give one piece of advice to an ambitious data professional who wanted to lead, what would it be?

misha trubskyy: 27:08

I would say you, you have to learn, so it's a great question. I like it. You have to understand, you have to make an effort. To understand how what you're doing on the technical side fits into your company's larger picture. And not only in from a system standpoint or I have a model that pops up. Adjuster is doing something right, and that gives them recommendation. You have to take what you're doing and literally go through every decision that people are making based on your. Numbers or your model or whatever it is that you're producing, who is doing it? Why what is the downstream from that decision? What is the upstream from that decision? And you and you have to do it with everything that you do. And you have to learn how to make it part of your workflow. When you are I, ic, data scientist.'cause when you take on leadership position, that's where you shift in your focus to, right? You shifting your focus to from a hundred percent technical details of every project that you're doing, right? You're not creating features anymore, right? You're not building pipelines anymore. You're trying to think, how's the product of your team fitting? Into the company at a very detailed level because that's how you're going to be judged as a team and as a leader in the end. And I think that's missing with a lot of people. People are so focused on, what language I'm programming in, what tool I'm using, what the kind of pipeline I'm building. And they forget that we're not doing it for academic and fund purposes, right? We're doing it for very specific purpose that has to feed with the larger purpose of your organization. And I think that's where any leader that, especially in the data, I mean in data science specifically needs to start with, right?'cause if they fail at that, if they fail at that, if they're not able to do that becomes an issue down the line as they progress.

ben parker: 29:25

They sent it. Okay. Wait, just wait there, Isha. So I just need to, what, have you received a digital receipt? They need to know what, what to, what would it be? His phone, email. Sorry, Misha, I've just got check. We can edit out.

misha trubskyy: 29:38

No worries. Yeah, no worries.

ben parker: 29:41

Yeah. Received it. He has received it. Oh, sorry. Sorry, my dad's just doing something for me. Oh, sorry. Nearly it out. Oh, it's all good. Okay. Cool. Cool.

misha trubskyy: 29:51

Yeah. Yeah.

ben parker: 29:52

Alright, ready. So we'll move on to the data topic and obviously we're gonna look at the current hiring challenges in today's market, which obviously is yeah, a challenge for many businesses. So from where you sit, what are the big barriers companies face today when trying to hire great AI and data talent?

misha trubskyy: 30:10

So I think I think there's a lot of people on the market, right? So if you just want to hire people, right? That's not that hard. Obviously when you want to hire the right people or the or, or people that work with you and gonna produce value, that, that's where the challenge, obvious challenge kind of comes, come, comes up. I've been on a hiring spree for the last year and a half give or take. And what I see is a is is a couple of things. A, there's a somewhat of a disconnect between what companies want. And what people can offer, right? A lot of companies you look at other companies or or at the general job offerings, right? They're looking for this unique person that does that knows all of the technical things under the sun and can do everything that's possibly, could be done. And these people usually don't exist, right? And on the other hand, you have candidates that see these job descriptions and create resumes and create profiles that are just filled with keywords. So you open a resume and you have a profile that has, 300 keywords. And you just look at it and you say, there is no way you know this. Maybe you've seen the name. So it becomes really hard to separate the realistic kind of the skill that people have from the from the desired skill that you need actually to to to have on on, on your team. In addition, a lot of skills are getting outdated pretty quickly these days, right? So you okay, if you know something in 18 months, 24 months, that skill could be pretty gone, right? So I think that was one of the bigger challenges, taking the candidates and really finding okay, who, of which of them, because you have, you, you post a role and you get a thousand resumes, the challenge is gonna, okay who of the Southern, even at face value could be a good feat for your company for your group rather. So that's been challenging to go through to go through something like this. And, we, when we hire, we are trying to be specific about what is it that we're looking for. But we're also have a limitation, right? We can't be we have to know that, especially when you hire more junior people, that you have to be open to educate. You have to be open for people to come in without some of the technical skills that you might need on the team, right? You might want to. Think about hiring people that have the ability to learn, have eagerness, to learn instead of having the exact set of skill sets that is probably not actually accurate. That is probably not something that people can actually utilize in the, in their work. So I think that's been one of the, there are a lot of challenges, but there's probably the starting challenge, the first challenge that you face when you're trying to hire somebody. Yeah.

ben parker: 33:32

Yeah, I think obviously the technology is, obviously its benefits, but also it's creating challenges for firms. Obviously now you've got. Will do the, obviously your chat, GBT CVS match just matching keywords, which, and there it's

misha trubskyy: 33:46

oh yeah. Yeah.

ben parker: 33:47

not obviously anyone can have. You wanna share your end there. You wanna share value what you've done. You wanna share a story of your expertise and,'cause like you said, you can't be an expert in everything, but, and each business is on a different data journey and they'll need different skill sets at different times.

misha trubskyy: 34:04

Exactly. Yeah. Yeah. And when I look, yeah you picked it up really well. You said it really well. And when I look for, when I look at candidates, at res at resume review stage, and at the later stage I'm looking for somebody who is genuine, right? Who, here's what I've done in my previous role, here's the technology I used. Here's the outcome. Here's the outcome that I pursued. Here's the outcome that I created. Here's how. You look for people who actually have done things. And not only that, they're not robotic almost, right? They're not just executing on some sort of technical requirements. They understand the value, they understand the connection that they bring to to, to the com, to the to, to their current or previous employer. And they are ready and confident and competent to bring it into your group as well. So that's, and it's challenging. It's not that often that you find people like that actually.

ben parker: 35:06

Obviously hiring is massive change for anyone and I think

misha trubskyy: 35:09

yeah.

ben parker: 35:10

it's finding people that have got the problem solving skills, and I guess the way data's going now as well, in my opinion, is. So you've got a lot of tech tools now can do heavy lifting. You need people that have got the, now it's being able to communicate with the business and like in doing, actually understanding the data and what use, like using what model is gonna be the best to get to the right solution the quickest.'cause it's, you can't just say use LLMs for everything.'Cause it's not necessarily gonna be the best route. I think it's just getting the common sense and that intelligence to solve your whatever problem it is the quickest.

misha trubskyy: 35:43

Yeah. Yeah, exactly. And common sense. Common sense is the common sense is actually going to be at the highest value that you can get out, out of people because you often have, so one of the problems that that you have is that, you mentioned LLM, right? LLM is not gonna solve everything, but also sometimes the problem is actually solved. By taking an average, right? You don't even need a model. You don't need anything complicated. You just need to understand the problem. Understand what data available to you and understand what type of decision is are people trying to make based on your right. And more often than not, you don't even need anything complicated. You just need maybe a table, maybe you need to pull some data and just give intelligence to your customer instead of building a model. Customers internal customers tend to come to you and ask for something complicated. Can you give me an AI solution that tells me something? And, you talk to the customer and say, Hey, why don't we look at the average of last three months? Would that be solution to your problem? And the often the answer is, oh, you know what? Yeah. I think if I knew what happened over the last three months on average, I think I know what's gonna happen next month. So how do you know? And you need to find people that are able to have this conversation, but also be okay with executing on it. And not necessarily going and trying to find the most complex solution or the most fancy solution, or the most solution that sounds the best for today's kind of world. And that's critical. Critical thinking and and being and being yeah and being able to execute critical thinking against yourself as, as much as against kind of the problem that you. So that's that's an interesting skill and that's what we're trying to find in people when we interview and talk to people and getting them, to join the company.

ben parker: 37:41

Yeah, I think it's like just being authentic, like real.'cause amount of times we've had people pass interviews'cause maybe during the interview process, obviously there's so much to remember in your field. Like it's, and sometimes you, there's sometimes you're just gonna forget things and you'll end there. You just need to be able to. Tell, say if you're interviewing not the interviewer, like this would be the process, like how I'll get the answer, not just go down a. Spend five minutes blabbing about an answer which is made up and you're completely wrong. And they, people just wanna know your process thinking.'cause if you can problem solve, that's what businesses are gonna look for.'cause you can't remember everything in statistics, data science, it's too much. It's more about like, how can you solve the answer? And it's that critical thinking element.

misha trubskyy: 38:25

Yeah. Yeah, exactly. And these days you don't, one of the good uses of LLMs and is to help you and not to remember some of the things. What you need to do. You need to understand the concepts and to understand how they used and what they mean, but the exact formula or exact line of code Yeah. That's why we have technology these days, you don't have to remember that. And in fact, I don't think anybody does. So it's a, I, when I interview I, I often ask I often ask some technical question and I'm not even looking for the correct answer. I'm looking for the logic behind it. What does some measure mean? Especially if people mention it during their interview, they say, oh, I measured some sort of metric. I say, okay, can you explain to me why? And EE even if they're wrong, I am. I'm looking at the way they explain it to me. The way they the way they outlined why the problem was and you're right. In during interview you can get nervous, you can get confused, confuse things. That doesn't matter. In another thing that I do, if I have a PhD candidate, I always ask about the dissertation. And that's an interesting, it's, it is so interesting to hear people and you and you could see the communication ability right there. Can you take. What probably is pretty complex topic that you wrote dissertation on. And can you explain it in the simple terms? And a lot of people really do well, but a lot of people can't really, and I, I've been into, I've been in interviews where somebody would say, oh, you wouldn't understand. Okay. That's an interesting response to to, to given an interview that's probably not a person that you want on your team that refuses even to go there. But yeah, but that's been, very helpful when you try to pick out candidates those questions, simple questions that people can't really execute on. Which is interesting to, to know right before you hire somebody.

ben parker: 40:25

Yeah, definitely. And not storytelling is so important nowadays.'cause you've gotta, like you said, articulate your technical knowledge into business terms.'cause stakeholders, they just want it easy to understand, don't need to impress them with all these new technology like jargon. They just need to know what the outcome is basically.

misha trubskyy: 40:45

Yeah. Yeah. I, the, yes, the out, in in, in many cases, people people don't, when you work with your customer, internal customer, in many cases, nobody cares about the technical aspect of it at all, right? We, as data scientists we like to focus on the technical aspect, or, I did this model I use this fancy technique to come up with features. But your customer doesn't care, right? Your customer wants to know what's the need for them? How is it helping them do their work and they can't care less. What type of fancy technique you use and that's where the value comes out. Can you actually explain it to a customer? Can you entice them to use the result of your work? Because that's how you become successful in, in, in what you doing.

ben parker: 41:34

So we move to the next question. What's one misconception about data hiring that holds leaders back from attracting stronger candidates?

misha trubskyy: 41:43

I think, yeah I think we talked a little bit technical, right? So the technical skill is the demands for technical skills are being over overemphasized, right? People are focusing on specific set of narrow technical skills that candidates are selected on. They are missing on understanding the softer pieces of it, business acumen, communication, critical thinking, and so on. I think that's the biggest that's the biggest kind of disconnect and misconception that that companies have. Yeah.

ben parker: 42:22

Yeah, so

misha trubskyy: 42:23

because yeah.

ben parker: 42:25

say, one thing I think is missed is like attitude testing or personality testing.'cause that's gonna show. like whether they're gonna be able to step up their game.'cause obviously, let's say comple business is so complex nowadays. Everyone's got different requirements and so there is a gap in a market with skillset. So people need to be able to adapt for different companies when they move. And it's, I guess many companies test people's personality traits and see how they got that ability to do that.

misha trubskyy: 43:01

Yeah. I think, yeah, I I think personality test is more it, I think it comes down to having your larger group talking to this person and trying to put the person outside of kind of interview zone in some of the interviews getting a little bit more personal. I, I haven't been exposed to personality tests, to be honest with you, so I haven't seen I, I know that some companies make you take them and score you on some sort of. Scale on a personality? I haven't seen those, I haven't been tested on those. And it would be interesting to see how how well this test do when it comes to picking specific personality, how realistic they are in terms of picking specific personality traits that you potentially want from a person. But yeah I certainly, in my world we are accomplishing it by making sure that people talk to their peers, right? People are interviewed by, not only by managers, but also people they'll work with or people interviewed by the stakeholders. Trying to understand that part of the job and get into that personality trait as close as possible because it's a tough one. I agree with. I agree with that. How do you know? How do you do that exactly? Yeah. It's not super clear.

ben parker: 44:20

Okay. And obviously companies say there's a skill shortage and candidates will say Companies are too picky. Why do you feel there is a disconnect? And who's right?

misha trubskyy: 44:33

It's, I think so I think both are right in, in, in many ways, right? The co the companies are two p. So it's a matching problem because if you think about ideal state of the world where you have a list of jobs on one end and a list of candidates on another, and you can pre-assign almost right people to jobs, right? The, that problem would go away to some degree. But. In the reality. I think the both are both are correct because companies are looking for the specific skillset, which doesn't really exist. So for example, I could come out on the job market and say, I want somebody who is a great data scientist and also who knows claims. And that's a short, that's not a long list of people, right? It's just by design. It's not. Somebody who looks at my posting could say, oh, that's a very picky person because they ignoring all the great talent that comes from banking or startups or, or what have you. And and the candidate side. You have a lot of people that are on paper very very prepared, right? But for example, never dealt with dirty data, right? In my world, data is bad. Just bad period. And you get somebody, especially younger candidate that comes out of the academic environment they usually deal with a single table well prepared data. So how do you pick a person that has that experience and to have that experience? You had to work a little bit in some corporate environment where data is for, where data is. Even structured data is extremely dispersed and unstructured, and you have to know how to put it together. So by the end of the day, those two statements are somewhat correct. I think the challenges in the market itself, the market is not efficient and maybe recruiters could somehow fix it. Maybe there is some mechanisms that could be utilized to, to make the market itself a little bit more efficient. But as of right now I still think it's gonna stay this way. I think we as employers, will look for set of relatively specific skills and. People will not have them right at the same time, it becomes a matter, it becomes a game of picking the right person with the skillset that they have and teaching them and making sure that they are proficient enough to acquire, the skills that you need them to have. So that's, that's what I think about this particular dichotomy, so to speak in opinions.

ben parker: 47:40

Everyone's, like I said before, everyone's on a different data journey. If you've got a small data science team, and like you said, you need that knowledge of claims in your team, obviously your requirements are gonna be. Rigid, aren't they really? But if you've got, say, a data science team of 20 people, you can probably, I'd be open to hiring people from different industries.'cause then they can learn that claims expertise. So I think it's. I think where it is we get, you said you get a lot of people applying for jobs where they're not right. It is, everyone's on a different stage in their data journey, and I think everyone, businesses are just, they've got different requirements and that's the problem. So I guess on the flip side, could businesses invest more into staff?

misha trubskyy: 48:23

Oh yeah. Yeah. And I think they should. And I think this technology that, that is coming up over the last two years or so that's actually helping with it, right? Because it becomes much easier to invest. So it's it's your, we always look at buy versus build decisions when it comes to vendors, right? But I think it, it could be applied to, to to, to people as well. And it's important. So it's a great, it's a great question. One of the problems I think that LLS and AI, modern AI will bring is that people are ignoring junior stuff, right? There's a lot of you, you read that, that say, oh, the simple tasks are going to be outsourced. So why do we need to hire junior staff? And it'll backfire in the future, right? Why? Because where are you gonna get expertise? Who is gonna do the work in even three year term, right? When some of the people who are currently doing the work will start moving on and doing some other things. So it's extremely important to actually acquire junior staff that might maybe not necessarily proficient enough for for some of the work. But can be but can be taught. So it's build decision, right? You get junior staff that probably not going to do great immediately, but you build them and you teach them to do the work that that they'll be able to do in three to five year term much better. This being said, I think that the basic skillset is still extremely important, right? You can't, I'm a big believer in in technical education because even with lms, even with all the ai, you still need people with STEM degrees, right? You still need people with mathematics, mathematical backgrounds, so that they actually have. A deeper understanding of what is it that they're doing. And it's extremely important. It's I've seen too many examples where people without without the basics they they have a hard time actually performing well in, in these roles. So it's a, to answer your question it's a combination, right? You still need people, you still need to hire people with best, these basic technical skills, right? But you should be in a mindset where you are teaching them to do your work, your specific work that you are asking them to do over the longer period of time. So the investment here is extremely important. And I think I applaud those companies. That do that they have analyst programs. They hire out of college into analyst programs, do rotations, and then spread around analyst and then teach them and train them to be really great contributors in the future. So they're not thinking, okay, this guy's gonna contribute in 18 months. They think in five years down the road, those are the best places to, especially for younger, out of college, out of master's degrees, people to look for jobs.'cause that's where the company has a culture of longer term thinking. And that's very important.

ben parker: 51:36

Yeah, definitely. And a lot of the sort of fourth and five country companies that we've helped, the minimum for data science roles, they, the minimum they want is masters.'cause again, you've got that theory, you can apply the theory into a business context, maybe for the more techie like data engineering roles, just like a bachelor's is more, they're open to a bachelor's. But I think it does, if you've got that solid. Understanding in statistics, like you're gonna just, especially in your field in insurance, like you, it's numbers. You need to be good. This is obviously a lot of people that go try and do the shortcut route, do data camps and things like that, but you just not, in my opinion, all the big companies want people that have got minimum masters because you bring so much value through that knowledge.

misha trubskyy: 52:20

So I in, in some way agree with that? I think, I have I have experience with all sorts of all sorts of people where you could have a really strong bachelor's in mathematics, pure mathematics and be extremely successful because. That gives you the level of abstract thinking that just helps you in this world. Pretty, pretty pre, pretty well, right? And like you said, some of the camps and even some of the kind of master's degrees out there, they give you really really surface knowledge of things, right? They're trying to cram a lot of subjects into one year sometimes, so people do have exposure to get exposure to some of the theoretical pieces, but not really enough, right? And what really stands out is a solid kind of ungraded background. Either either in, in engineering. Or computer science. Or just pure math or pure chemistry or something like this. Something technical on average. More technical. Yeah. But I see, I see that, yeah, people, a lot of companies saying, oh, we need a master's degree for you to have and I, I would, I encourage people to be, to go to, to look a little bit beyond potentially what degree says and okay what does it mean? What is your undergrad your degree mean? As it's applicable to the technical side of your job and non-technical side of your job. And then make those calls versus to say, oh, we just need a master's degree.'cause I could give you a lot of examples where a master's person is going to be inferior, even in technical skillset to somebody with a solid bachelor degree. From like a solid institution. So yeah, I would say that's not a, that's probably not the right measuring stick for everybody.

ben parker: 54:14

Interesting force. So how does an unclear strategy hold data quality or like weak organizational maturity impact the company's ability to attract top talent?

misha trubskyy: 54:27

Quite a bit. And I think the short answer is impact it impacts across the whole the full spectrum. On, on one hand, if you have a mature organization with there, there are a couple of things here, right? You could have organization that is mature in a sense that. They made a decision to be digital and they have a good plan in place and that they hold good strategy and strategy needs people and they're doing a good job hiring those people and creating the organization and moving forward, right? So that's organization that is well set up to be an advanced data-driven team within the next, 18 to 24 to 36 months, right? So that, that organization will will start hiring good leadership that, that, that leadership will start hiring good team members, technical that will be aware that organization may be not super mature technically but will be able to build up and go from there, right? So that's one thing. If the organization is technically immature, that creates a bit of a problem because a lot of candidates are asking. What tools you are using are you what kind of toys, I'll be able to play with can be efficient, right? If you don't have answer to those questions, you're gonna lose a lot of candidates. You're gonna, you're gonna, you're not gonna be able to attract some of the better ones because better candidate would probably come, a better technical candidate would probably come with some knowledge of core tools like AWS or Azure, right? And they would want to be able to use those tools and some people come with more specific requirements or specific interests. If you have an immature organization in on technical side you're gonna have a problem with some of the better technically savvy candidates. If the organization is immature. Then it's a problem because you have an issue on both ends of the equation here, right? You don't really know what you're looking for yet. You don't really know how to hire and you are putting out this message. Any experienced candidate would be able to find out if your organization is immature. You ask questions such as, gimme example of a data decision that you made as an organization, right? And it's very easy to pick, pick out when it's a real, or when it's something made up. So you're gonna have a real problem hiring strong candidates. The weaker candidates, it's easy because weaker candidates often just want a job and they'll take whatever. That was in front of them. But a stronger candidate, people with multiple job offers, potentially people with choices, you're gonna have a really hard problem finding those and retaining those because, people could get hired and then in, in three months they'll be out out of there because you guys don't know what you're doing. So I think it, I think it affects a lot. It affects how you look for people. It affects the type of people you can you can attract. And it affects retention. Short term retention is actually, I've seen it to be a problem with people leaving after under 12 months because they are not satisfied with one of the aspect of. Of the company either, there is no data strategy, there is no understanding of tool usage or tools are limited or there is no, so all sorts of problems that could be, that could lead people leaving or not being interested in your role. That comes from immaturity of the organization or technical immaturity, right? Either data immaturity, kind of strategy immaturity or technical immaturity. So that's, that's something that you see quite openly in, in the market these days.

ben parker: 58:18

Do candidates ever ask you about strategy or maturity?

misha trubskyy: 58:21

Yeah. The, that, that's actually a good filter because you want people to ask these questions, right? Yeah. You want people to especially when you hire for high, for more senior roles, you want the person to be interested in that. You want to ask, and not only tech, a lot of people ask about technical maturity'cause they want to know, okay, I know, I know Azure, but I don't know AWS is it okay, is it okay that, that I can learn? But the benefit, the best people will ask you about your data strategy. You, you ask you about the examples of the projects that you are working on, how they do almost interview you and you can filter those. You, I think to me that's a great signal for any candidate they can. And then you have a conversation that, is it. Is it registering with a candidate? Is it just a question that they ask Chad GPT to ask or it's a real question that they understand my answer. It makes it a great conversation and it's a filter for me for sure to know that people are asking questions like that.

ben parker: 59:22

Yeah, definitely. It shows it shows intent that they actually interested in your business as well. And it's, yeah,

misha trubskyy: 59:27

Exactly. Yeah.

ben parker: 59:28

shows like end day. It shows that they're switched on, in my opinion, because if they're asking these types of questions they see a career down the line, not just a job. If you get what I mean.

misha trubskyy: 59:38

Yeah, exactly. Yeah. Yeah. People interested. Yeah. Yeah. And you want people that are in, that looking at, five to 10 years down, down the line not, or I'll be out on the job market in 18 months and I'll go look for something new or else and they're interested in being with a company that gives them that opportunity, that gives them that learning opportunity and that execution opportunity to be there for five to 10 years. And it's good to see. It's a good, it's a good test for the candidate.

ben parker: 1:00:05

Yeah, so when you interview Candice, what signals tell you someone can actually drive your business outcomes?

misha trubskyy: 1:00:12

So I look for, we talked about all the technical stuff. I don't I don't resumes that they're just filled with keywords. If I see the resume that. Keyword keyword driven. That's a red flag for me. But what I what I'm looking for is being a, being genuine, right? Being genuine in what is it that you are offering. And you can even see that from resumes pretty clearly that you, you see the resume of a candidate that trying to be proud in their work outcome. I did this, I accomplished that, and I helped my company with this particular problem. So those are the candidates that, that I prefer. And then when you talk to them, you are trying to really find the genuine response genuine response from standpoint of. I really like what I do. I'm really proud in what I was doing, but I really also understand what I was doing. I really understand the problem. I understand why I use certain technique or certain technology. I really understand how it's affecting my customer, right? Either internal customer, external customer. I really understand how I measure the outcome. Here's the outcome. Why is it important? I understand that, and so those are the kind of candidates that, that stand out. And you can see them really easy. I ask questions. And obviously it's easier with the more experienced people, with the people that have, 10 years or so under, under their belt, when, but even for the younger candidates that that that have a few years of experience, you could still pick that out, right? You can still see. Where they're just following some sort of protocol and they were told to do this model and that's what they did, and they just, throw it over the gate somewhere or the fence somewhere and somebody's using it. I don't know why or how, versus to say, okay, I actually took a pride in my work. I made sure that it helps my customer. I made sure that I know exactly how and know exactly what I did and and that's the kind that, that I would go for. Versus, versus just somebody who's, technical technical at face value, and that's it. That, that, that's where it ends, so to speak.

ben parker: 1:02:37

Yeah, I like that you said about like the authenticity of individuals.'cause there's stories you hear where people, like individuals are using AI during interviews. It's come on. Like

misha trubskyy: 1:02:46

Oh no, that's, yeah, we've seen that. Yeah we've seen that already, so I, oh, yeah. Yeah. I didn't, I was I saw, I saw it somewhere. I read about it and I'm like, no. That's seems, I thought that's, that seemed so stretched. But we have seen it. So people are clearly re so, and that's when genuine response comes into play, you can know, you can absolutely hear when people are reading from charge GPT answer. And that's and certain, certainly that becomes a immediate no. That becomes immediate rejection. That's nobody. But we have seen it. Yeah. Oh yeah.

ben parker: 1:03:24

because even

misha trubskyy: 1:03:25

and even.

ben parker: 1:03:25

they, they pass the interview, start the job, they're gonna get found out and then they're gonna just lose their

misha trubskyy: 1:03:31

Exactly.

ben parker: 1:03:32

Go, I know people wanna impress people during interviews, things like that, but you to look. You need to show your worth. And Dave, you can't answer again. Interviews are stressful. There's a lot to remember. If you can't, if you freeze, just be up front. So many, we've had so many people like interview for top firms as well for us. Just said that I froze. I just said how I'd get the answer. And hiring managers that like that I think if you gotta be realistic, like people will. Just freeze during interviews. But when you hear the stories of people just cheating, it's just, it's so bad practice.

misha trubskyy: 1:04:05

Oh it's such a it's bad, but it's also so obvious. It's not, yeah. It's it's hilarious how obvious it is and. And yeah, I mean I had I, I personally hadn't had experience like that, but folks that interviewed had some screening interviews for my team and they were telling me that, you interview a person and it's, they obviously cheating and you just sit there, you don't know what to do with it. And yeah, and it is hilarious. Yeah.

ben parker: 1:04:32

Yeah, no. Okay. And then obviously on the other side, retention is now just as hard as hiring. There's a lot of opportunity as well, which don't help. So what generally keeps top performers engaged once they join a company?

misha trubskyy: 1:04:46

Yeah. Reten, I think retention for the next maybe 12 months will be a little bit easier because, this market is tougher, so people are not as eager to jump ship. But retention, yeah, retention has been an issue. And and we've certainly seen people jump in, ship some within, 18 months and even faster. And I think, and I think there are three, three, three, four things that, that I think are important to people that they look at. A, we talked about strategy, right? Do, is there a strategy for the company when it applies to my subject matter, but also for the group, right? Can I see myself being here in five years and executing on the strategy? Is it clear is it transparent? Is it attached to the business, right? And if people see that. They'll be they'll they'll be more likely to retain obviously individual, right? Is there, is the title structure, is there a way for me to grow? And some people and I think it's a mistake, right? A lot of companies, they force people to be a manager if you want to grow, right? That, that, that's the only way to grow is to become a manager. I think it's a mistake you have in my world there's a, in, data science world, there's a lot of people that don't want to be a manager. They just want to do more complex work, and they want to be, more independent work with stakeholders directly, but they don't want to be managers. And that's okay. So is there a structure, is there a title hierarchy that would allow me to grow and become, get to vp, senior VP level in terms of compensation, but still be. Individual contributor, world class or higher level in individual contributor. So that's extremely important. Management even with the strategy, even when everything is small, is clear management, and especially, you know perfectly well that the biggest predictor of retention is the immediate management, right? So I think that's one of the key pieces is your immediate manager and your manager maybe, are they on your side? Are they looking out for your interest? Are they creating opportunities for you to grow, to do new projects to to challenge you? Most people, especially younger, they want to be challenged. They don't want to just repeat the same project over and over again, right? They. They want different subject matter, they want more complex ideas. They want to be able to challenge themselves, right? And that's and that's, oftentimes I really like when people are self-driven when they can create those opportunities for themselves. But it's it's tough and it's it's often on a manager in a, on your immediate manager to create those opportunities for people to make sure that they're growing to push them, to grow as well as to help them grow, right? Some people need a little bit of a push. Some people can help themselves, but you need to be there to, for both of those situations. So those are the things I think that going to create a better retention. Yeah. Right now, the market is, on one hand I do see positions. We, we have positions. I see other insurance companies hiring for data science role. But on the other hand you, you do see companies, a laying of people, right? You have, announcements coming up every week that this or that company kind of laying off people. So market is tough right now. But but retention is is, it will be a challenge. It has been a challenge. It'll continue to be in a challenge. But it's a solvable challenge, right? It's, and it's not about money, right? People defaulting to money only. Obviously money matters to, to a large degree, but it's about environment about the future, right? If you can have the future in your head. For this person and this person will more will be more likely to stick around and be a productive member of your team.

ben parker: 1:08:52

It's also like obviously lifting recruitment. You need to like, what's the why? Why do I want to join? If, why do I wanna join this insurance firm? Or why do I wanna join the pharmaceutical firm? It's finding that why, and then making sure, obviously the company have got that. Like you said, strategy for them to excel.'cause people want to develop, they want to grow, they want to learn like once to stop learning. That's why people leave.'cause they get in. It's like ground dog day. They don't want the same day every day. They want to be growing their careers.'cause then as soon as you learning, you are gonna, you will, in my opinion, you and you are paid market conditions. You are gonna stay with a business.

misha trubskyy: 1:09:28

Yeah. Yeah, exactly. Yeah. And yeah, if you start to stagnate or stop learning for a lot of people that's a signal that, okay, something is off, something is wrong, right? And I need to look. And yeah, and sometimes people are not necessarily even actively looking, but if they feel that something is wrong they'll look and something potentially comes up and it's hard to say no to opportunities oftentimes. So you have to create internal opportunities, essentially, right? And make sure that internal opportunities satisfying and fulfilling and promote growth. For individual person.

ben parker: 1:10:02

Yep. So looking 12 to 24 months ahead, how do you see the hiring landscape evolving, and how should leaders prepare for the future?

misha trubskyy: 1:10:12

Yeah. I think, it's an interesting idea. It's interesting question. I think so right now it depends a little bit on how this AI technology pan out, right? Like I said at the beginning, I am I'm a fan of technology. I like the promise of it, right? I haven't seen the success, the over overwhelming success yet. And we've seen the studies from MIT from Stanford that, that are mixed, right? You have a lot of failures with implementation. And I think that's how that's what's happening right now. People are, people were extremely enthusiastic about AI technology and I think there is a, has been some over hiring of people for AI roles. But now it's leveling off. People see limitations, people see failures. Not a lot of AI projects are actually being put to production. A lot of them not, don't go that far. So I think for the next 12 months, probably, people will be waiting for the next stage of what this technology offers. And and that will probably, cool the market to some degree, especially if we are expecting, there's a lot of talk about recession and a lot of talk about the bubble in AI technology and stuff like that. So especially if that some of that neg negativity becomes reality, that certainly will have a big implication on on, on us. In another thing is I think, you and I talked about it. Already is that, the technical skillset, right? You have you, you have this range of technical skills that people put themselves in. You, it used to be that you are a statistician, right? Then it used to be that you are, there was a statistician, then there was a econom nutrition, right? And now you have, almost data science as a skillset is almost becoming a little bit obsolete. You don't have generalists anymore. It's too it's impossible to be a generalist, right? Are you LLM guy? Are you m lobes guy? Are you AI governance person? So that will but that is a very, right now that's not very well defined yet. I think that needs to. Settle down a little bit and and that will indicate, the future for the next 12 to to, to 24, 24 months again. Yeah. And I think it's coming back to that build versus buy again, right? Is it that we need to build our pipeline from the junior talent now knowing, investing, knowing that the junior talent will not be able to contribute right now and investing the money into junior talent, or we kinda wait for talent to develop and maybe, 24 months from now, buy, develop talent that will help us to grow to the next stage of the business of what we want to do. So I think those are the. Those are the driving practice and I think it will be a little bit cooler job market wise for next 12 months at least. Just because of how economies operating. Just because where we are in terms of hiring and what requirements are and what the expectations are. I think it will be a little bit less active than the last, two years or so when it comes to job market.

ben parker: 1:13:42

Okay, brilliant. Misha, thanks for joining us. It's been an outstanding chat and really appreciate your time.

misha trubskyy: 1:13:49

yeah. Yeah. Thank you. Thank you for having me.

ben parker: 1:13:52

Cool. And then to everyone listening, if you found con today's conversation, fable, please do support the show. Give us a follow, leave a quick review or even share it to a friend or colleague. Just help people discover the conversations we've had. And thanks for listening, and we'll see you on the next episode