Episode 117

Aleksejs Plotnikovs on Mastering the Art of Tech Hiring through Data Analysis

TIM: We are live on the Objective Hiring Show. Today we're joined by Aleksejs. Aleksejs, welcome to the show.

ALEKSEJS: Thank you, Tim.

TIM: It is wonderful to have you here with us today. And where I typically start is just trying to get a sense of the guest and who we are speaking to today. So I'd love to hear just a bit of an introduction about yourself so our audience can start to understand who they're listening to.

ALEKSEJS: Sure. Absolutely. So my name is Aleksejs, and as you said already, I've been in data all my life. It's happened, very timely that graduating university, software engineering became one big theme. And so I got this career start in software engineering, specifically in data, database development, and things like this. And then it took me, through all these types of famous places, to realize that Nokia was one big company with a lot of presence in the world. And then I moved to Oracle technologies, again, an hour kind of milestone for all the data people, used to be. Most interesting data warehousing solutions, 20 years back and so on. And then, I've definitely get a consultancy arm, like I think many of us. So I've been part of Accenture as well. And so finally I've landed at Microsoft, and I spent then 15 years at Microsoft. And my role in Microsoft was really to build false data foundations for the company to create that notion of data being used by the business units by, building that competitive advantage and this involved, technical capabilities. It's involving building their large team across the world, which would be a central team those days. For the company and many other aspects, but it was really about getting value out of the data, and so I think this happened really nicely with Microsoft. So I love that story. I love that story to the extent that I even wrote a book about this. So it's called a data management strategy by Microsoft, available, you know, on Amazon. But it was really that type of reflection of how do you build these data capabilities, which are very new, even still new for many companies, and how it's evolved over these years, with AI, with so many breakthrough technologies coming every couple of years and shaking the entire thing, yet you're still focusing, like, how do I get value out of this? So that's Aleksejs.

TIM: Thanks for that introduction. That's awesome. And I think it's an interesting place to start. Now that you've painted that picture, I'd love to get your thoughts on something that I've always thought was a little bit odd. And so I just love to hear your reaction to this. So most leaders would say that finding and hiring the best people is, if not their number one goal, not one objective, certainly in the top three most important things they're focused on without the right people. Everything else is a lot harder. And you've just painted this picture of all these advancements in data happening over the past 20 years, with AI now being used in all these different bits of life and technology and products and marketing in day-to-day everything. And yet not really when it comes to people, like most of the hiring decisions companies make tend to be, if it's a spectrum of data-driven to intuition-based, very far along the intuition-based. Into the spectrum. This varies. Like when I've heard of people who have been at Amazon and maybe some of the big tech, they do it in a more rich, regimented, kind of data-driven way. But I think for most businesses, these are really intuitive-based approaches. Is that going to change? Do you think we're getting to a stage with the development of AI and data where actually we could have the same quality of data about people as we do about other things? It might open up a new wave of kind of making data-driven decisions about people in hiring. Yeah, what are your kind of thoughts about that?

ALEKSEJS: Yeah, that's a great question. I think. We should get some advancements, certainly. The power of AI in connecting the dots or extracting some, some dependencies, some interdependencies of, human knowledge, human profile, it certainly should help. On the other hand, what I see today is probably the number of large challenges in the companies that operate with data is really on that kind of a product manager, technical product manager, or whatever we call it. So the person who builds the bridges between the domain knowledge, the business kind of knowledge, like why the company exists, like what it wants to do. We have that data and then actual technical teams, which are responsible for the technical implementation of the data. And so in these places, I wouldn't say that intuition is the number one thing, but it's certainly a very complex knowledge, which is difficult. Qualify or classify very explicitly. It's a mixture of, like, how do you understand that the person really has that domain knowledge or has that business knowledge? Now you can check on certain data and aspects, but it's still not the technical assessment you do. You do more like a bridge between the technical capability of the soft skill capabilities like selling or storytelling. And so assessing these objectively, with some AI capabilities. I think we are a bit far away from that. And this is where I feel that this intuition, it's not an intuition in a pure way, but you know the feel of the managers who've been through it, feel that type of a personality feat versus knowledge versus experience. That's what kind of is under that intuition, so to say.

TIM: As you were describing that kind of person, I thought of the bridge between the domain or business knowledge and the technical side that resonated with me because it's a role I played in a business in the past where I knew enough about the domain and had enough technical skills where I could do a bit of both and crossover and wear both hats. What about this idea? If the technical skills can become almost commoditized now with a large language model, you know, will Claude be doing most of the coding in a year, and you're just prompting something else to write the coding? Is there almost something to be said for the domain experts who now don't need to interface with technical people because they're interfacing with an AI, and now maybe that's opening up new opportunities because you don't need those two very different people? Skill sets that rarely overlap.

ALEKSEJS: I don't think so for a number of reasons, honestly. I think that with the whole help of AI, and I'm really a big believer in AI technology and like what it could do for us, I think it'll take a while to really get to the point that it's not like an assistant, but it's truly the lead way of the development of the technical capabilities and so on. And it's still about so much of understanding the technology behind it to be able to be. Relevant to those business needs. Yeah. So we see two Davises in a very simple conversation, which happens, I think, in every room across most of the enterprises in the world: the leadership comes and says, Oh, we need to have an AI product, right? It could be for an internal user or external for customers. And it sparkles a lot of those conversations, and it's actually first and foremost goes in understanding what's possible with the technology and how this could be enabled. And I think this will not disappear. So people, first of all, should be able to explain the technology to understand what is possible, what's not, and where the limitations are. Otherwise, they will be drawn into the wrong directions and wrong decisions, and then they should see. Okay. How does this really maps to that business opportunity? What is it that we can scope out and crystallize and power with that technology, right? And what is it that we're missing or what we need from an implementation perspective? So I would say we are just maybe getting a little more help going forward with streamlining our work with automation of certain tasks, but I won't see. In a year or two from now, the radical change in the way we work

TIM: I interviewed someone recently gentleman called Artem Corin, who runs a business called assembly. ai. And they are like fireflies. There is a note-taking app that you invite to your meeting. It does the transcription of the meeting. It does the summarization. It does the action points you agreed upon and integrates into task management software. So it goes to the next step. And he was a really interesting character. And the way he described the change that we're seeing now is that, imagine we had this special coworker who we needed to set up some conditions for them. To be able to do their best work, maybe they needed, like, a certain kind of desk, or they needed to be in a certain quiet space of the office, or whatever. So we made accommodations for them, but they're amazing. So they're worth it. That was the picture he was painting with AI that we need to rethink how we run businesses to leverage this infinitely scalable colleague that we're about to have and rethink from the ground how we structure our businesses. Do you agree with that? Or do you feel like we're not at that point yet? How do you view things?

ALEKSEJS: We're moving towards that point, I would as of today, like this what 25th of february I would say we're not there yet. All right, and it's a matter of certain things. The technology itself is pretty much there. So if you think about this digital employee, this multi agent type of capability, which creates a certain identity and very task focused and actually successfully tasks focused. So the current AI capabilities we use are generative AI models and so on. They're very good at resolving tasks. And so if you define this and you base it very much on the understanding of the language or understanding of the text, so again, where the original technology comes from, that's pretty good. Where it comes in is the adoption, right? And the adoption means that to integrate such a capability into your work, you have to get pretty much everything digitalized in a company. So you have to get the digital processes up and running. You also have to get that ability to tap into various aspects of the data, not only to source it, kind of work. Read-only way: If we think about the classical analytical dashboards, which every company so far has already gotten, it's just a read-only. So you generate some insights, but you rarely have the mechanism that pushes immediately some action based on this, right? So here we're talking about that we actually do a lot of. Physical move, of the knowledge of the data of the processes through the digital agents. So this is not enabled in the majority of the companies. And then I would say also the cultural aspect of this, right? So you have to get that cultural adoption that the people would really be ready, knowledgeable, and educated about how to work with the digital employees. So if you take this adoption kind of a. whole concept and how much we need from a neighborhood perspective, how much we need from, people's side of this, then we are very far from this concept. Albeit from a technology perspective, yes, we can push this.

TIM: That's very interesting, that analysis then, because straight away I'm thinking, okay, so if you're in a large organization, where of course things don't change that quickly, there's the cultural adoption you're mentioning, there's the kind of pushback, risk aversion, data privacy, lack of digitization, and all those kinds of things. If you're a startup, you don't have any of that. If you're starting right now, you can define your business afresh and go, We're going to record every single meeting ever had in this company. 100 percent of everything's going to be online, purely digital. We're going to start from afresh, building everything from the assumption that most of our colleagues will be AI, not humans, which I doubt many companies have ever started that way with that assumption. I wonder if then there's going to be some sudden wave of startups that are just going to come through and wipe out any, I don't know, 20 or 30 year old tech company that's still stuck in what will now be the past.

ALEKSEJS: That's a great point. I think in theory. Yes. As far as you're in control of your own world in the startup, surely what you just described is happening already somewhere and probably pretty actively happening. But then what was going next is that you were living out of that world of your own controls. And so you have to get either to. A space where another company is already dominating, already working with existing customers, and so on. You start to serve the others. And if those others don't have those digital-ready interfaces, which you can easily tap through, then your work is not really valued, right? To me, this is this moment where we have to get the overall momentum across multiple digital platforms. Industries, which then moves more or less along just to have a couple of startups, which have completely different environment and they rely on a lot on digital agents and, digital employees. That's fantastic, but does this really make an impact for those whom they serve or those whom they cooperate with? And does this change those large companies where a majority of the people work? So that's the thing, that again, it's good to have a super tech. Which leads the way and shows, like, what could be done, but to really get that motion with the technology to, hey, to say that we are leveraging now with digital employees successfully. I think this is still years to come.

TIM: What about in terms of the hiring process itself? Have you started to dabble with any AI hiring tools? Have you seen any candidates starting to use these tools as part of their application process? And what's your current read on AI in hiring?

ALEKSEJS: I think it's currently a little bit of that kind of try and fail thing. So candidates try to use AI to improve the readability, improve the positioning, and improve. Certain correlations of the thing, then employers using AI to do absolutely the opposite, right? To unwind, what was the truth? Yeah, so what's there, like certain scanning mechanisms again? So I think we need to get to a certain maturity in that space, right? And I'm not sure about regulations as such, but I think it's more about the maturity. In seeing, like, where it really helps, because in theory, both the employee, a future employee, and the employer, they are interested to get a good match. And so this is what I would say AI might try to help the most is to create this good match based on maybe certain things, which are not so visible for the human or for the recruiter, right? Because, again, when you think through, I, reflecting back on the processes that we've had at Microsoft. So if I need to hire somebody, I would first write a certain, job description, which is fully in my language, in a way how we use it for ourselves. Yeah, it would be fairly so from the industry perspective, maybe a little bit, but it will be full of jargon and internal acronyms and stuff like that. Then, we would have a recruiter who would pass this through and say, Oh, on the market, you should call this position slightly different, or You should remove these acronyms and replace them with the other. So it starts. So if you look then on this modified description, you see already, okay, it's not exactly mine, but it's also not for recruiters anymore. And so it's in between, which doesn't really make sense anymore, neither for me nor for him, but nevertheless, we go with this. So the hiring site, right? So we post this on LinkedIn. And then again, there is no match automatically or no mapping between those skills that the candidates would have with what you post on LinkedIn. So it's again this type of dancing between what does this really mean, these operations, operations are used in so many contexts, or I have to do the data analytics again. It's a very different scale depending on what you're hiring for in the context. So I think a lot of these things get lost in translation at the end. And then this is what I see as a challenge where AI might help. In theory, at least, through navigating that language and that ability to see the context. Yeah, so like the tone even or the connections between certain capabilities and highlight them up, to say out of those 120, 200, 300 resumes, which you received. There is some human objective in this, and there is some AI assessment in this. And maybe this AI assessment can show certain things that humans will not see.

TIM: Yeah, and I suspect this technology is being built at the moment. I've certainly spoken to a lot of people about this. I know some applicant tracking systems have started to dabble in that kind of AI based resume screening. I feel like one of the key problems is going to be with this kind of matching challenge. You got a job on one side, and candidates on the other. At the moment, the data available to do that matching, whether AI or anything in between, is crap because someone has written a resume about themselves. And on a resume, I could claim to be a rocket scientist, and nobody can really say no at that point. And as you described before, that job description becomes almost, through time, some kind of Frankenstein description that maybe doesn't actually represent perfect reality. And it's only a summary anyway; you can't go into all the details. Is there something to be said for we need to get better quality data on both sides of that matching? To have any hope of doing it more accurately with AI or any kind of automation

ALEKSEJS: I would say so, but I would also say that it depends on the context of the hiring, right? There are industries that matured very well. And they do have a very solid understanding of what those positions are. What are those skills that I have hiring for? So my wife works in retail per se, and retail is already a very well-organized environment, so you have this pretty clear classification of what the people's role is in the shop, right? There is a store manager. There is some area district manager. So those roles are very well defined. We've been there for years. So they evolved. But they have that maturity and understanding, and when we go into these technology things, and a lot of roles today on LinkedIn and so on, they are technology-related. And this is where we're coming to that, what you just said, this mapping, this understanding of what has been done in one company with the one terminology is possible. Fully transparent to another company with very different terminology, but because of this terminology being so different And there is no translation layer as such; this looks like absolutely two different things. This is what we have today, a lot, right? Or we have this mismatch again of those scales. Where are people calling out certain skills and certain proficiency in those skills? There is not that type of a universal way to assess them; there is not enough certification. There is no enough maturity in easy way to say what is your level of proficiency, let's say, in data quality. All right, so what is your proficiency in data analytics? It's super dependent, actually, on which tools you use and what company you work for. So it's too much into the context. In the weeds versus upfront in the resume

TIM: and to go back to your example before, you mentioned your wife working in retail, and maybe that's a more settled environment. The job descriptions are all consistent across different companies. But does the same problem still exist yet? You don't have that matching between different companies explanation of the same job, but still it's it would be a challenge to match. A job is to a candidate as one of hundreds of resumes because the resume is just a representation of someone about themselves. Like, you could include or exclude any information you want, which may or may not give the kind of full picture, if

ALEKSEJS: true, but I think it's still much easier, because there is more of that repetitiveness of the things, there are more of those kind of templates almost to say, which fits. Yeah, so even if a candidate takes the existing position on LinkedIn, it looks like what they're asking for. And they start to reply in the same way, in their CV, in their resume. And so this creates this type of reusability of the same things going through. And then what I've seen on the other hand is in technology companies, and in many places where we do need quick technology, we see that it is a mess. There are no standards. In a good way, standardized job description. It's not about an HR work doing in-house standardization of the disciplines and so on. It's important, but I'm talking about mapping this to the external world. Can we have that place like LinkedIn or something else, which will eventually be this translation kind? https://otter. AI scales, which you need in a company versus what's available in the market, and kind of clearly identify how they work, what's the different maturity of those scales, and so where you need to go. And I think a lot of our think team as well, and there is more ambiguity, I think, generally, right? If you take back those roles, which are well situated for years, they have less ambiguity, and we've always had a technology-related world, and data and AI in particular, as well as the leadership roles. There is much more ambiguity, and this ambiguity chimes in, and it creates that extra complexity and extra mess in trying to map the things for.

TIM: Part of the origin of the ambiguity is because certainly even in my life in data, the roles have changed completely. Like there, there weren't so many roles now. There weren't so many roles 10 years ago as there are now. They've been divided up software engineering. I'm sure 30 years ago. It's not like there was front-end dev, back-end dev, database, or DevOps. It was more like a Jack of all trades kind of role. And so maybe just cause the roles have changed so much and they're always changing and it's hard for companies to keep up as well.

ALEKSEJS: Absolutely. And this is what we're saying is the proliferation of these roles. And the definition of some new roles, which kind of nobody even understands, like what's behind this. So some company comes up with an idea that they would like to have an, I don't know, AI evangelist. Yeah. So who is the AI evangelist?

TIM: Good question. Are you,

ALEKSEJS: Maybe, let's look at a job description, right? So that's the thing. Yeah. So you pick up something that maybe is very innovative, but it doesn't have any grounds, right? It doesn't have any mapping kind of capability to the rest of the world.

TIM: Speaking of that, actually, I have spoken to a few analytics leaders recently who have mentioned they've noticed a trend for companies that are trying to make their first data hire, their first analytics hire. These are companies that are maybe 50, 100, or 200 people. They haven't ventured into that yet. And they run into a bit of an issue, which is that they don't have anyone in their current team who could help define that role. The people who would help define that role would be the person they're hiring. So they're stuck in this kind of circular death loop. And so they don't really know what they're looking for. Have you seen any way out of that for these companies? Do they need to engage, I don't know, an external consultant or something? Because their talent team won't know the answer to this either.

ALEKSEJS: Yeah. And this is a difficult question. And, I think certain help externally would absolutely be needed. The way of using this help probably Will be the most important right? So you said external consultants. I have nothing against the external consultancy; I would just be very curious about how to map it to the exact objective, right? So this consultancy Ideally, it should be connected to what the company wants to achieve with that first data approach and so on. And sometimes it's very specific. Also, they lack certain capabilities. So we've been using this externally, and now we need to bring it more in-house or double down on this or something. But if it's really like they never touch that and they just start to explore the world of analytics or something, then it will be important to see that it's a match with the business expectations, right? Because the business expectations from this or leaders of expectations could be quite different. And that's, I think, the biggest issue here, right? It's not about not finding the talent. There is a lot of talent actually in the market, I think from this perspective, but it's finding whom you really need. Do you need more as a kind of startup leader who will organize everything? Do you need a great storyteller, which will help, defend those investments, and land it very well with the growing business needs and so on? Do you need a technical person? Because you really need to go more into those technical foundations. So I think we rarely will find those unicorns, and even if the companies are finding those unicorns, then in your example, those types of businesses, they will immediately say, Wow, that's too expensive. So we're going back. Okay. So if you have this. Unicorns can do everything, from leadership to technical acumen and so on, but that's like too much money. So then you need to find all the straight-up things, and that's where the connection to the real business needs and, like, the fundamental why question, like, why are you investing suddenly into the data? If you need a quick thing out of this. Maybe it's not about having a full-time position. Maybe you just should buy this as a service or something, right? So that type of level of granularity would be very important.

TIM: You mentioned the why and coming back to that. And I feel like if I think back to my last 10 years, really, of working on this business and working in a previous business, hiring processes that derail and go badly. I feel like the root cause of that is often We didn't think of the why in enough detail. We didn't really meditate. Look, what are we trying to hire someone for? Because we've got 500 euro tickets, and we just want to bash through them quicker. Are we hiring someone because I'm doing some tedious manual task, and I want someone else to take that pain off my plate? Like, why exactly are we hiring? And then that leads to, okay, we're hiring for this role. What exactly are their responsibilities? What exactly are their skills? And I feel like if that step is ever skipped over or done in a, yeah, here you go. Here's a job description that I found from three years ago. Just use this, then everything after that is almost doomed to fail. Have you noticed if you noticed this?

ALEKSEJS: We've been quite lucky at Microsoft doing this entire hiring process very differently. And I will not say it's a best practice, but I will say that it at least had no question of why. And to explain this, we relied a lot on the externals. So every team, would have by default pretty small amount of full time employees, but we would have a lot of externals and we would have externals as just agency employees, or we also could have, the suppliers, like a big names working closely with us. And we would observe that delivery and that talent for years. And as you observe, as you see how we grow, people take on more leadership roles, they create some innovation, and they really stand out in this. So you constantly see those types of top talent things appearing in that whole big delivery space. And then you can pick this up and say, Oh, actually, you know why? I know why. I want you specifically because I've seen you for three, five years delivering great work in this environment. Now we understand that the person would have terrific IP already with them. They are an absolute hit because we loved their contributions and everything. So we'll then poach over to the Microsoft, and that's exactly what also was for me the same way. So I was an Accenture consultant. I was brought into the Microsoft environment. They absorbed this for a couple of years and said, Hey, come over, right? There is no question of why in this case, but this is not a strategy for everybody; on the other hand, this type of approach, where you can really observe people for a long period of time and you see how they're performing, is when you're choosing the best out of the best already. That's a big privilege to have this type of capability.

TIM: And speaking of the approach at Microsoft when it comes to hiring, was it a, I'm assuming, fairly structured approach where everyone went through like a consistent set of interviews and interviewers were trained and you're asking specific things within a framework? Was it that kind of approach as opposed to the more gut-feel intuitive? I'm just going to get a feel for the person and give them a job or not.

ALEKSEJS: How do you think?

TIM: Definitely. The first one I'm assuming was it a hiring machine? Is that what it was like?

ALEKSEJS: I would say it's 30 percent of a hiring machine and then 70 percent of kind of selecting by that feel and feet. type of approach. We had a structured approach, let's say, so as not to derail from this completely, we had a structured approach where the people would be interviewed. By a different person. So it's like probably four or five people are in our team. We would assign each of them some specific set of skills to watch. They wouldn't interview just for that. They wouldn't start, the conversation with a candidate saying hey, I'm interviewing you from the perspective of, technical depth or something, right? But they would observe certain skills and, like, how they are more common and so on. So it gives us the ability to remain human in interviews while also focusing maybe on one or two things only without kind of overanalyzing or overprocessing everything that we hear and see. We will do the recap afterwards between all of the participants and, highlights and strengths and so on. But, at the end, it was so much about a feat, and this feat, as you can sense, was not defined exactly by those capabilities, so if something was fundamental and missing or in conflict or kind of looking shaky, then for sure this will be called out. But if everything is equally good across the candidates, we will discuss a lot of that kind of team fit. Cultural fate, and I will tell you it is a good reason for this. All right. I think that the branding, like this Microsoft brand, It's so strong, or is still very strong, that the people would come to the company just because they want to be hired by Microsoft, right? This is the fundamental kind of thing for many, independent of what the position is. The location, the skill sets, the interest, and actually completing this work within that given team environment—they just want desperately to be hired by Microsoft. And these types of things, we had to watch for this a lot. And this is where you. You assess it objectively from, candidate skill set perspective and so on, but you also focus a lot at the end and okay, would this be a fit or would a person just use this for any opportunity to get into the company and then, move immediately to some different roles and stuff like this. So you have a lot of this kind of back processing, which. Is really what then influences that decision of higher on a higher.

TIM: And just so I understand correctly, if you sense that someone was just, they're just desperate to work for Microsoft. That is, is that a green flag or a red flag?

ALEKSEJS: That's a great question. It depends. In most cases,

TIM: sensor.

ALEKSEJS: yeah, in most cases it will be more like a red flag, honestly in the sense that it's just over the rights and overshadows everything else. So what we are always looking for is that. Yes, the whole willingness to work for the company, the interest, but it's more of an interest in that role in the team, in that impact to be created. Yeah, this is what will be the most fundamental and most interesting. Can we, if we hear, is a little bit too much of that constant kind of notion of, Yeah, but it would be great to work with such technology leaders"? Something else comes up, with compensation things or some other flexibilities and so on; this then triggers, like, okay, what's the true motivation? So this notion of what's the true motivation? And, for us also involves teams hiring process always was, important, right? So we do this investments very deliberately. So we want to get the best out of the best and have it immediately. There are just, I think, many. So that purpose, a match would be very important. And if it's overly, overly indexed on that company, in a more global way. Versus in the particular role and ability to create an impact within that role. Yeah, we will see it more as a red flag.

TIM: Just thought of an analogy to dating, and I'm going to throw this out there, and it's just popped into my mind. So it may make no sense, but it's like Being in love with the idea of someone rather than them their self if you see what I mean You want someone who is actually interested in the specific role in the team rather than all Microsoft. Oh, the brand name. Is that

ALEKSEJS: Yeah, exactly. Yes. Indeed.

TIM: One thing I'd love to ask you about is interviewing because most companies, I would say the main evaluation tool that they would use in the hiring process, really still is interviewing with humans. Yes, they might use a skills assessment, IQ test, or personality test, but I've not met a company that didn't use interviews. and didn't place the majority of their weighting on the interview performance. So I think it's still very important. You must have done a lot of interviews yourself. You must have sat in on a lot of interviews where other people were driving. If you think across your career, what is separating a really great interviewer, as in the one asking the questions, from an okay interviewer? Huh.

ALEKSEJS: If I were to say the number one thing, it's empathy and the ability to be empathetic to the candidate. And let things come up, open these conversations, and have this ability for people to feel good and talk about what they can do. Yeah, I've seen a variety of the strategies I've seen. You know, people creating some stress on the candidates—I don't understand why. Like really, I've also seen a lot of that structured approach, or people desperately want to get some answers, and then at the end they give some free room. I don't think it works. You don't get much of that. What's the person actually doing besides just getting some stuff to you, which you are even unlikely to be able to process quickly? So I think that conversational ability and being empathic to the candidate and letting people open up, talk about their stories, talk about their examples, and get to the point, like what they actually are as people. What is that baggage of the soft skills, which we bring in? What are those technical skills that bring us in? We can talk about this again, and the technical skills may be deeper. In some more specific assessments But if we're talking about the interview link as such, it won't be really about the technical skills most times. It'll be maybe a couple of questions just to quickly arrange; do you know what this is? What's that, so that type of things but then it goes really into that personality Deeper and for this you need to get people open up.

TIM: Yes. Because I think part of the difficulty in interviews is that, I'd say both sides of the table have a bit of a mask on, like we're not necessarily being fully 100 percent transparent. We're not just putting all our cards on the table. And yeah, you want to really get to the truth of who they are, as you said, their motivations, and what they're interested in. Do you have any techniques for doing that in particular, any ways to almost, yeah, make the candidate feel relaxed and calm and really open up to you?

ALEKSEJS: Yeah, it's interesting. What we try to do typically is create; we try to explain a lot about what we do, right? What is the role? What type of work do we do? What's the impact? How it operates and so on. And you start to see already this reflection from the people, if they like it. Yeah, if they hear something that matches their aspiration or work of doing this, they jump in and say, Oh, that's you know, that's exactly what I was doing, right? And they bring those examples straight on and so on. And so you lead this conversation more by creating a space where we can talk and talk about their achievements versus you going brutally up front and saying, Okay, tell me your top five projects you have completed. Something like this is awkward, right? So I think that's what created this true ability for people to shine with their realistic and relevant examples. Also matching what we're trying to create in our site as the, as a potential working environment.

TIM: I wanted to lay one. Idea at you. And this is partly again based on a conversation about recently with another guest where they described their overall hiring philosophy for 20 or 30 years was to identify the diamonds in the rough, which for them were the candidates that would be almost like systematically undervalued by the market in some way. And for him, that was people who would be really great at their job but interview terribly. So let's say very cripplingly introverted people, people who maybe aren't great communicators in general, maybe some people a bit more on the spectrum and just don't really want to deal with the difficulty of meeting all these strangers and having all these questions probed at them. Is there something to be said for the fact that we almost like over indexed on interviews as an evaluation tool and we're accidentally filtering out all these people who would otherwise be amazing, especially in very technical roles, not consulting roles where you have to deal with people all day, but like super technical roles. Is there something to be said for being conscious of the hiring process?

ALEKSEJS: Oh yeah, I'm totally with you that the hiring process itself should be super conscious, right? In terms of what kind of skills are required for what type of job. The cultural background as well. The cultural, not only background, but the cultural settings. And so when we worked across the whole world. And so I had people in Germany, in France, in Australia, in Singapore, and in the US. And so it's a very different cultural background and setting of how people actually work in, generally, in the country, what's important to them. So when you do this type of interview, you have to get that whole kind of setting upfront, and you have to understand You know what? You will be asking different questions, or you will be asking, let's say, questions differently to the people in Singapore versus people in the US, and so that's something which is important: to always have this very clear distinction, like what is what we're hiring for, what is what we're looking for from a scale personality influence perspective. You know how important it is, right? So what we don't pick up You know to this point where you're like, Oh, I just don't like that person. Yeah, it doesn't matter. Do you like a person or not? We have to first understand if the person is actually a good candidate and qualified to do the job, right? This aspect of dislike doesn't have any significant standing in that space, and so this is also for the managers and for the people who are participating in the interview. It's so important to step back from this; like, I really liked that person. Okay. What were the skills there? Oh, I don't know. We just had a great conversation. Yeah. For some roles, it could be perfect; for our sales role, this could be a perfect testimonial. But maybe for some other roles, it's not.

TIM: Yeah. And this is where personally I feel Like we shouldn't get too hung up on things that are inherently subjective because I feel like they could be twisted very easily. So let's say you've got cultural fit, which I think would be the main one where, unless you've defined that quite concretely and you've really thought, No, here's the five values we're selecting for. Here are the questions we're going to ask to evaluate these things, unless you box it somehow. I think it's so easy for someone just to come back with that kind of feedback, which is basically, I don't like them. They won't be a good cultural fit here, but yeah, like, why is there got to be some kind of evidence at least shortly?

ALEKSEJS: Exactly, and I think this election of also the personalities who are doing the interview is also important here. So if there is a if there is an opportunity to select, different and, people with a different leadership style for doing those interviews, I would absolutely recommend it. And I did this, so we had a number of peers. Which are different people from the way they operate in lead, the teams, and so on. And it was always super important to get that, different perspectives and, accumulate them and decide afterwards, but at least to capture it, not to go into one very, skewed direction because, everybody who was interviewing was the same style type of persona.

TIM: So a bit of diversity in every sense of the word in the interview panel, because then you're going to get a wider array of views. Aleksejs, if you could ask our next guest one question about hiring, what would you like to ask them?

ALEKSEJS: I would ask what was their best hire and why.

TIM: A fantastic question, and not one we've had and not one I've asked any guest. That's a great one. And I'm, it made me think about my own hires, and I've in the past tended to think more about the fails rather than the successes. So that's a nice reframing. You've given me actually,

ALEKSEJS: Let's be positive, right?

TIM: Yes, exactly. Yes. Okay, great. I'm excited to ask that of our next guest at some point in the next week or so. And I'll share the answer with you. Aleksejs, it's been a great conversation today. I've really enjoyed speaking to you and tapping into all your knowledge, skills, and wisdom in this area. And I'm sure our audience has enjoyed it as well.

ALEKSEJS: Thank you, Tim. Was glad to be here.