Episode 19

Artur Yatsenko on the Modern Hiring Landscape in Data and Tech

TIM: So, Artur, when you think about hiring right now in the data space, in your view, what are the biggest challenges?

ARTUR: Yeah, so I think there are two components to it. The first one is we definitely want to hire the right mindsets, like being specific about when talking about the data and talking about the tech, and that is finding people who are curious about that and also have a good sense of ownership, which I think sometimes could be lacking. And then collaboration, of course, you're looking for somebody who is a team player because I've also seen use cases when people are really good experts, but they can't really work in a team or can't really share and spread the knowledge, so that is one that is definitely about the mindset and the values, so to say. And secondly, I also think that the companies, so the era of like really hyped gross, I think is not there anymore, so you really need to hire consciously and sustainably after all, because just to give you some examples, so for instance, back in 2022 we had around, I think, 90 applications per job, and in 2024 we have around 200 applications per job. So, which also tells how the market is really heated up and some of my previous experience, I'll be also hiring analytical teams through multiple different locations, but then it's not happening that fast anymore, right? So really, people and the teams you're trying to be more conscious about who you hire, what kind of impact it brings, and how you can particularly maximize the effort and bring the best value to the company, so I think these are the two things that really shape a bit of the hiring landscape right now.

TIM: And so you mentioned a few things there around the mindset that you typically look for in these characters: curiosity, ownership, collaboration Is it currently more difficult to find that than it was a couple of years ago, or is it an ongoing challenge that's always a sort of skill set that's in shortage?

ARTUR: I think it's not particularly in shortage, but right now, also given if you have a lot of candidates and you need to see them through, you really need to look out for this particular one, and it's trying to devise some of the cases that can help bring this up, but ultimately some of the things are easier to test in a way when you do the interview; when you do some of the talks, some of them are quite more complicated too. And I think it's just the role of the hiring manager to really set up what is most important in the candidates and set up the guidelines at the beginning of the process, also together with some of the company values that somebody might have, so that'd be a guidance that you need to set up beforehand as well.

TIM: And so setting that up beforehand I guess as part of what you mentioned around consciously thinking about the process, which is not diving into it, and maybe the hypergrowth stage, where we just need to hire 10 people and get them as quickly as possible, this is a more considered approach. where you've almost thought ahead of what exactly you need, and then it's a case of designing the process to hit that

ARTUR: Yeah, absolutely. I can also get an example, so when I was working in the company, it had different offices around, so like they said there was one in Berlin, there was one in Singapore, and we also had to hire a lot more data professionals, so I also couldn't keep track. I was not hiring all of them, but I also couldn't keep track of who was getting hired and in different locations because there was such a fast-paced environment, such a fast hypergrowth, really. At some point, you're like, Okay, oh, you have a new joiner, right? How do you make sure that they also integrate into the team itself? How do they follow Let's say what are the best practices in a way, and that was also quite challenging because you did need to spend a lot of time on onboarding an individual. Yeah, so I think right now is definitely more prudent; it's more, as I said, conscious hiring in a way. Yeah, because also the budget I think is getting, you know, a bit smaller as well. I mean, you definitely need to—we all want to hire actually the best professionals in the markets, but given also the market is like quite heated up, you do need to look over a lot of the different applications and make sure this is the best pick for your organization, and the process is more thoughtful than I think. It used to be before

TIM: And for some of those particular mindset characteristics, if you will, the curiosity, the ownership, the collaboration you mentioned, you typically evaluate that during the interview process. Are there certain ways, if you think back now to the candidates who did well or didn't do well, or the certain ways they would or would not demonstrate those things in an interview, like certain, I don't know, red or green flags?

ARTUR: I think if I look back on some of the experience, and also, yeah, I mostly interview for the data roles, but I also sometimes interview for tech roles, so like helping out my other colleagues who were interviewing for different positions, like head of engineering with different, let's say, software engineers as well. I think for me I had been almost primarily focused on the way the person communicates and how they are able to structure the salt and the whole salt process, and of course having the communication around a clear message and also the message as they can adapt to different audiences, which is rather simple. It sounds really simple, but let's say if they're like a security engineer, like explaining to me some of the really maybe complicated protocols for security, which I don't really know about, and they don't really realize that Yeah, for example, I'm not an expert on security as such, right? and they don't really adapt the message, so I'm like thinking, okay, how that would have been easy or hard for them to really communicate it to the business stakeholder, right? I still understand, of course, but it might have been complicated for different audiences. So I think people who can really tailor the message, who can show the way that they think generally, what is the software behind that, they definitely have more advantage. Also, on the other side of the coin, there was some asking for some examples. I recall there was a candidate for one of the positions who had, I say, maybe more than 20 years in software engineering, but the way that person came to the interview was like, It's a bit more like with an attitude: I know everything, right? I know everything I've done that I know that you might ask whatever. So it was like, really, that was a bit intimidating, but also saying I'm not really flexible in my mindset because I already know it all, ultimately I said no to that particular application because it would be really hard to calibrate with the group. People who actually are curious and want to learn more from each other, right, and rather spread the knowledge than being flexible, also to change their mind as well

TIM: Yeah, so there's a bit of humility there; there's a bit of a need for, as you say, kind of teamwork and sharing your skills with others, and so that's really interesting that some candidates maybe can't necessarily do that even if they have the right skills. Yeah, and clearly demonstrate the right skills. If they don't have those other pieces, it's hard to make a hire. You mentioned in passing that there's been an increase in the volume of candidates, of course, mapping to the market. The fact that unemployment's maybe a little bit higher, there's been some layoffs, so now there's a higher volume of candidates per position. Has that caused any challenges in the screening stages, or are you changing the way you would screen candidates in those early stages of hiring?

ARTUR: Yeah, so I think, as I mentioned, definitely the amount of applications had gone up. It definitely puts a strain on you as a hiring manager and the recruitment partner that you have because you definitely need to go through more things, but of course using some more processes, perhaps to automate, but nevertheless, even, you know, myself, would go over 100 applications, and the thing is, like, you need to, you know, trim your ears and analyze really to look out for what stands out, and it is really challenging sometimes because, in the end, how do you know that you didn't miss any, let's say, gold nugget in the pile of those CVs after all? So I think the process takes longer, of course, and I would also prefer to go through those applications to know, okay, so let's maybe talk to that or that individual, so say, like, from the perspective of the time that is being used, it definitely has increased. And generally the hiring process I mean, it is not an easy—I mean, it's not always a straightforward process because it takes like different multiple stages as well. You, as the hiring manager, are also the one who's helping set up how many stages there will be and how the whole thing will go, and it could take, luckily, maybe one month to like multiple months to hire a really fitting candidate as well.

TIM: One thing we've noticed over the past few years is that the hiring process itself and the speed are directly determined in some part by the current market conditions, so we would have seen, I'd say, like peak zero interest rate COVID expansion in Australia maybe like 2021, where companies are hiring crazily. and in Australia we had a weird situation where we had the borders shut, so we had no migration, and in the tech community in particular we rely heavily on skilled migrants. I'd say the vast majority actually would be skilled migrants rather than local-born Australians, and so it created a weird market condition whereby there were like thousands of software engineering ads and three out-of-work software engineers, so it created this strange condition where then companies are like, Let's get rid of this interview. Let's get rid of this test. Let's just hire as soon as possible. Have you seen a kind of reversal whereby there's now maybe a bit more risk aversion? You can be a little bit pickier; maybe you can make a slightly longer process and be able to get slightly more certainty in who you're hiring. Is that what you've seen play out?

ARTUR: So I was saying that definitely changes, like from who is hiring, like for which different positions, as an example, there could be something really niche, like security engineers. As an example, I have sometimes a really specific skill set, which is really hard to find on the market; at the same time, there are also different locations. For instance, it was in my company; we hire also in Valencia, so we have a tech hub in Spain, but also I felt there were challenges, and let's say, not being in the local market, because I don't really particularly know the Valencia market, which sometimes is also challenging to find the candidates there as well, or let's say targeting some institutions, universities, and schools like for fresh graduates, but depending on the level, but I think to answer your question as well on these stages that really do it really depends like from the position that somebody is hiring for we have been not too many, and I've seen that at the beginning, like we did also, for instance, ask candidates to solve the case study, but that will take also like some more time with it and I think nobody wants to really invest the time in their home take-away case study anymore, so we took it back; we took it away. Now the process is a bit more later. We also have seen the general increase of satisfaction rate among the candidates regarding the whole process over the past couple of years as well. So now we're also trying to condense a lot of the interviews, which were maybe in one day, so you have a technical interview, you might have some architectural design interview, and then some kind of collaboration principles, like cultural fit, let's say, interview as well was in one day, and then basically giving an answer hopefully sooner to the candidates. and I think what the candidates also really value is they really value the transparency, so not only, of course, the amount of the stages they have to go through—this definitely has to be there—but also it's about what they can expect in the job and how they can, you know, grow. What technology do they use, and how can they progress within the career framework? And we also try to demonstrate that through a public covering framework that they can take a look into the tech creator showing the technology that we use as well so they can see if this is really even exciting for them to work within the same environment. Yeah

TIM: Yeah, I feel like transparency over the job and hiring process is probably one of the lowest-hanging bits of fruit that a company could invest in to encourage candidates to apply and then continue in the process because a typical job ad is devoid of really anything; it's just like a bunch of tools and a bunch of requirements. often a generic-sounding description, and it's so predictable, which you've done hiring enough; it's so predictable what candidates are interested in. They'll always, or typically, ask that in that first interview call, so if companies can package that up front and tell people exactly what the remuneration is, what's the bonus scheme, and are there any share options? What's a day in the life of a team? Who do they work with? As you say, the tools and technologies that are actually used, what their metrics are, and how their career can grow, if companies can just package that early on in the process, I think candidates are going to be much happier to continue through and to see it to fruition. Can I ask you a little bit about AI? How could we not discuss AI? Have you seen it so far? Impact the hiring process, for example? We hear a lot about potentially candidates using it to create a CV to apply for jobs. What have you seen so far?

ARTUR: Yeah, so I think definitely for CV text, apply for applications, which I think makes all sense, and it's definitely great to use the technology; it's also important to use that surface possibly, right? After all, you can do it for multiple things, right? You can definitely just apply, like, Can you help me with the CV just to make it brighter in a way? And also, on a technical task, we, for instance, don't really do, like, home takeaway study now anymore. So which is it? Would it be harder for you to really do this with the eye, of course, but then it's more about the syncing process that we ask people to, let's say, even when in life, discuss or, like, trying to maybe draw some architecture diagram as an example, right? So a bit less than you can do, like with the I, maybe like in real time, yes, sure, of course, but still I think it's a challenge, and I think people, if they use that, that is the fun, right? You need to know how to use that properly; you need to use it to maximize your chances. If you're not using it, I don't think you maximize your chances enough, that's for sure. Yeah, and I think we still should be more on the lookout for the syncing process, right? And it's basically not how exactly did you get to that answer, but what exactly is the syncing process that took you there? I think that's what we all, as the hiring manager, are really looking out for more so AI cannot really substitute that because that's something on your own I think, but if people use that and use it responsibly, saying why not, there's no harm in it.

TIM: Is there any bit of the hiring process that, if a candidate had used ChatGPT or Claude or whatever, you would think Oh, I really rather wish you'd done this yourself; this doesn't give me a true picture of who you are. Is there anything that you feel like at the moment is off-limits?

ARTUR: Yeah, I think no, definitely would think about that, right? Because if we would, let's say, see the use, and a person says, like, This is completely my own, genuine idea, as an example, right? So there'll be something that you will not have really this trust created. If you don't have the trust created, like, of course, you will not be able to hire that person. But maybe I would then, you know, talk again, and you know Try to look into the different use case without the user interface and see how that's like subprocess really differs in the end. Are they able to come up with the seemingly similar answer, let's say, or not as well? But so far I haven't seen really any gross big manipulations, let's say, with the Ion technology that people would really present some of those, like, generated ideas as their own. Yeah, I think it's also all about the humility. I myself, I remember when I was interviewing for one of the positions, and it was like a case study; it was the home away take-home case study. And I made some mistakes, or like I made some assumptions that were pretty bold. And I didn't understand why the particular product was built that way, but then I realized, okay, maybe there's a reason I don't really understand why this is built this way. So it really toned that down, and I said, Okay, so maybe you should actually consider this particular thing, and then I realized there was a really legit reason that they couldn't, like, really allow, let's say, signups in the application for that matter, and I think it was that kind of a bit more being more humble. Not really, yeah, thinking that I know the answer completely I don't know why you're doing this in a way that's right, so it's just like thinking about the thought process and presenting that in a way that really helps also to assess yourself as the suitable candidate, and then you don't really have answers to all of the questions. I think that is absolutely okay to accept as well.

TIM: It's really interesting that you say that. Okay, you're more interested in the candidate's thought process than maybe the final destination or the final output, and certainly in an interview they'd have a chance to have a discussion, and that's where the thought process would come out, because you can dig hard to get that to come across in a test or a CV or anything earlier, but in an interview I guess it's easier. One thing I was thinking about recently was that a lot of software engineers, probably data scientists as well, would say that it's almost like they think as they're coding, the coding process itself is like them solving the problem. I've heard other people say that writing is thinking because you take these jumbled-up ideas from your head and commit them to paper. and it's only once you've written it with a hand or with a typewriter or whatever keyboard I should say that really those ideas become fully formed given now we're using a Claude a chat GPT to prompt where in a sense it's doing the thinking we're almost doing like a meta level of thinking Is that a concern that we're going to lose something that maybe we're not thinking through the problem-solving enough because we're outsourcing it to an LLM? What do you reckon?

ARTUR: Yeah, I think it's a really good argument, and yeah, definitely we—I think we were not going to lose that completely, but we definitely will dull that sense of thinking and solution space problem. Even I sometimes look at myself; I try to—I definitely don't use that for everything. But of course it's easier just generally to submit a prompt and then give an answer right then; they read through it, right, or search, and then instead of doing the search, you use the chat GPT or examples like so. I think that, yeah, we do like dial some of the senses of the problem-solving. If we turn to the prompting all of the time, we could also argue that it will be more of, let's say, like we solve really simple problems, but, like, with more complex ones, let's say we still involve our own syncing and such because that does require a chain of different prompts and responses, like dialogue and such, which is also fair. I think ultimately what is important is that we don't really lose that sense completely, and we're still singing about the complex problems, and then there's like multiple other sources that we just turn to instead of just submitting the prompt as well, but yeah, that's a really good point.

TIM: So I guess, yeah, we just need to develop a good understanding over time of each tool's own job and when we should and should not use this particular tool versus another one. I guess that will evolve as the tool evolves as well, probably.

ARTUR: Yeah, and it also evolved quite fast. I think if we're talking about AI, perhaps general intelligence might come as well, maybe in a couple of years. Right at the same time, there are all the different things the tools are using for different purposes. After all, as the technology evolves, I think we also have to evolve in terms of how we're thinking as individuals. So that's my take on that.

TIM: Yeah, what about in terms of your hiring experience as a hiring manager? Any big fails, any regrets that you have, or perhaps any fails you've seen, at least in your team or organization, which you look back on and go, We should have done things differently then?

ARTUR: Yeah, I think definitely one of the challenges, let's say if you really do the hyper gross, than some of the companies is that it's really hard to control, let's say, maybe the quality of the candidates who come in right at the same time when you really needed the given time somebody knew that might be quite challenging, but also I think you should have a lot more trust in your colleagues to say these are the people who are in poverty to take decisions, right? I don't really need to be involved in every part of the process, but as to like actual fails, I hate to disappoint, but I didn't have that specifically. I would think, but of course the biggest fail for me would be if I hired someone and I would need to let them go after a certain time, which of course you want to avoid because it's a really costly process after all, but I think most importantly, like what the companies had adopted right now that prevents that from happening quite often is that even you as the hiring manager, you take, of course, the last decision, but there are so many processes in place before that. The person who was on the team, the person who interviews with another team member, maybe from a different department, for some values of the company, there is like some leadership interview that if it's about the managerial position as well, so the thing is when I'm trying to say it's hard to go wrong right now. Even if I am, let's say, really stubborn, a hiring manager said they want to hire the person regardless, but if I have some of the red flags that were raised during the process, some other colleagues of mine would definitely think twice about that, which is good because it doesn't really rest all of the power on only me to really make the decision, of course, even though I have it, but I still have input from different people. Which means that in this case, the project process is a bit more objective in a way, so you really avoid, let's say, hiring people who are not particularly fitting, and I think maybe another story that was more of it was like a funny story when I recall it that was particularly from the interview process that I had also myself as the candidates Even more so, I remember I left the company like a couple of months ago, one of the companies, and then I got offered my own position back, which was really strange at the same time, and I saw that the person did not really do any research that I was actually working there as well. and I'm like, Do you know this is particularly my position in my department? You could have looked up my LinkedIn; it's just like there, so oh, maybe you want it, whatever. So there was no research done whatsoever; just reaching out to people out of the blue. and I was like, Oh, okay, that is so random, but that was like some of this strange experience when you've got to offer your own position back to you when if you love the company just a couple of months before it was, it was really fun.

TIM: So yeah, back to your point around hiring fails in your case, the lack thereof, so it sounds like there's almost a wisdom of the crowds effect where if you get enough people involved, all looking for the same types of things, eventually the candidate would slip up in terms of any red flags. It's someone's going to find some reason to say no, so it's almost like a process optimized to reduce the chance of a bad hire. Is that a fair summary?

ARTUR: I think, of course, you can't account for everything, right? even if there are candidates that technically sound like they show good collaboration values, but then they come up on the team, and they, for instance, are not engaged, they're not interested as much, right? How would you be able to also find that during the interview process maybe there are some examples that I don't really know about some approaches to identify that people can also suddenly lose the interest they might also take them? I don't know, like, too much time to really relocate this example or move start, like, working in the company; things change, and that could be a challenge, right? I think, of course, we tried to safeguard and do as much as possible to hire the right fit, but sometimes it doesn't really work out. I mean, you have only a couple of choices in this example, yeah.

TIM: And did you notice during those hypergrowth periods where you're just hiring like crazy, and maybe in those situations the process wasn't followed to the T, the process is probably shorter? As you said, it's happening in different countries with different people involved. Did you notice generally a sort of higher bad hire rate during those times compared to now?

ARTUR: I think, yeah, there was also happening. You could say that as well. That also led to, like, people will also be shuffled between different areas of expertise. They are self-responsible mostly, so let's say if you have a person, a candidate, maybe not as strong technically, but they are in charge of a really big domain, you would say, Okay, let's try to reshuffle that to a smaller one after all, but also there's a lot of, like, context switching. There's a lot of specific knowledge that maybe needed to be applied when it's talking about the product as well, but yeah, there definitely was the benchmark. It definitely was a different rate of the canvas. If you hire super fast and you cut on processes, this is what you get.

TIM: You make an interesting point there, though, that maybe thinking about it now, it's not that helpful to think of it as binary as regretted or not regretted because you just mentioned that actually you can move them into another team, which, thinking about it now, maybe is especially likely if you're hyper-growing; there's always opportunities; there's always new jobs being created. So maybe if there's just like some slight issue with a skill set they were missing or a mindset thing, that actually they could do a great job, but they just need to be in a slightly different role.

ARTUR: Yeah, that could also be the case, right? And I think the skill set in terms of technical ones is something you can always learn. The skill set in terms of the mindset, we'll say the mindset, is far more complicated to learn because it takes more time to really adopt the habits or change the mindset, really, and I think it's fair to say that we, as hiring managers, are trying to look for the mindset, particularly that really fits the company that fits the current needs in a way that also fits, let's say, the way that you can work with other people in general. So there's always been more hiring for the mindsets rather than for the hard and technical skills.

TIM: So then thinking more broadly about all the candidates that you've interviewed over your career, which must be in the hundreds, I'm guessing by now, if you had to separate them or think about what differentiates the successful from the unsuccessful, it sounds like the mindset piece and some of those things around curiosity and ownership and collaboration are the more common reasons people fall down, or there are some other reasons.

ARTUR: I think left for me was definitely more about how they are, how they persevere, and how they really go through with the challenges, and one example I can also give is I remember interviewing the candidate like years back that we were also asking for some technical case study for analytics back then. But then the candidates all didn't know for instance like Python like much SQL but then they were able to still build this case study using Excel pretty simple actually and they had that really you know urge they had that like really the necessity and they wanted to really work in the company They really showed you know everything they got and I think This person is still like working in the company already in the managerial position as well the technical skill was not a problem I mean you can do like with everything something different completely like a different tool not really knowing whatever some frameworks and technology but That whole the mindset first of all definitely and then, like the desire as well, that perseverance really showed up and paid off eventually, so that is something really great, and I think that's what makes more of a difference than

TIM: Yeah, and I guess in their case it also required you and the other hiring team to have an open mind that wasn't like, You must do this with Python with this particular package. You're more thinking about, as you said, how do they think about the problem? How did they solve the problem as opposed to solving it with X, Y, Z?

ARTUR: Yeah, it's about the thought process, I think, and it's about expiring. It's about brainstorming, and you don't have the answers always to those questions, right? But this is like for you to collaborate with people to generate some of those ideas as well, and often this is not something that is developed in isolation after all. You are not even if you are an individual contributor, right? which is normally the name of the title, but you always collaborate with other people. And I think this is where some of the ideas really are being generated. You can solve some of the complex problems.

TIM: Thinking about hiring in general, my view is that the way hiring is traditionally done can be quite subjective, not necessarily fair. I feel like often the best candidate might ultimately not be the one who gets hired, and there's some interesting and quite compelling research around things like these that have been done in various countries, applying with CVs to hundreds of different roles where the only difference in the CV is the name. So in Australia there's been an interesting study using Chinese or non-Chinese names and measuring the callback rate, and it shows that quite often, basically, if you don't have the right name, you're not getting a callback as often as you do, so I feel like there's a lot of things in hiring that are quite endemically unfair. Have you seen any examples of any kind of tactics to make hiring a little bit more objective, a little bit fairer?

ARTUR: Yeah, that's a good point. I think in Germany what I've seen is that nobody touches also the picture to your CV; there is no even way to objectify. Regarding the race and the gender, after all, which is good, I think sometimes you don't really know who else you speak to, right? And then basically what you only look into is the text. What are the credentials to say what the experiences are? After all, what are the projects that a person has done right? How are they also able to package all of that information? Because this is, of course, a lot of years of experience into one concise matter. So that's definitely one thing that I've seen, like thinking about some other things It's really hard to be objective. I think we try to be as objective as we can, and let's also accept that we also make mistakes as humans because ultimately we are a human-in-the-loop process, and that's something that we can't really avoid often of being truly objective. yes

TIM: You mentioned a human in the loop. Do you feel like AI will be taking care of more and more hiring soon, and a human might be there just to, as you say, be in the loop and check a few things to make sure they're going okay, or do you think we'll keep doing it in quite a human process?

ARTUR: No, I think it will still be human. It will still be in process, right? But like we already see and already have been happening not only with the recent rise of LLMs but generally even a couple of years back, the whole screening process for sure. It's like it's more automated. That is like really looking out for the things that you need in the candidate or like you set up as the goal. So, inadvertently, yes, I think there'll be more really Let's AI being used in the process as well. But we as humans still want to have control for sure, so I can imagine us still saying, Actually, I want to make a decision that I want to really validate because I really want to make sure that we don't really miss out on anything, right? But it definitely would be more on the rise. I can tell you that

TIM: Do you think it's a case of humans being overconfident in our ability to intuit things and going, I have a gut feeling this is the right person, and maybe eventually the data or AI will prove, No, actually, if you just remove yourself from it, the end outcome is going to be better ? What do you reckon?

ARTUR: Yeah, I think, yeah, as a human nation, perhaps we'll like to fight that decision of an AI, and we say no, by the way, I know better because of, like, my years of experience; machines cannot really label that properly, so yeah, I see definitely some skepticism and general perception to say no, I really need to rebuild it again. So I can see that happening, and yeah, I think we make mistakes as humans, but we also don't want to really acknowledge that we make those mistakes. The same thing with the eye makes mistakes; it sometimes does not. Technology makes mistakes because it hallucinates. But we, I think, as humans, it's hard for us to accept our own mistakes really and acknowledge them as well, yeah, so we will perhaps try to fight off and say, No, by the way, I think I made the right decision, and then they try to justify it as well.

TIM: I was thinking about something just this weekend comparing hiring to the way recruitment has been done in football, like soccer football, and it was, I reckon, only 10 maybe 12 years ago that English Premier League footballers, when they had an injury and had an issue, were flying off to Serbia to get a witch doctor to feed them horse placenta to fix their injuries. That was only a decade ago that was happening. Of course, now there's a lot more science involved, a lot more data involved in how they recover from injuries and how they go through their day. I wonder whether we'll look back the way we do hiring now in 10 years.

ARTUR: I think, yeah, maybe in 10 years we'll really come back to the origins in a way, and we say we need to have a person in the loop for every single step as well. It's just we as humanity maybe reinvent a couple of things, but then we also come back to other things originally because they're more comforting. They're also more known to us, but also, on the other side, there might be a rediscovery of, Oh, by the way, that was not as bad, let's say, as we thought it was. It didn't really need maybe like another improvement. Ultimately, it's hard to predict what it was going to be like in 10 years with the recruitment. Generally, people will evolve the skills of like individuals and candidates who apply and already are, and also for some of the positions, you need sometimes, like maybe the use of newer different technologies, like more general AI and everything else. So I think as candidates evolve, we also have to evolve to get those interviewers to also become better in the way that we talk to people and understand how we can recognize the bias, trying to be more objective as well, so it's a constant learning, right? And which is good to really try to figure out what works and what's not for you and your organization and how you can adapt to the challenges of the markets as well.

TIM: Are there any particular elements of hiring that you think could be improved with data, like measuring a new thing, like creating a new metric, or anything in between?

ARTUR: Definitely looking at the data statistics of what is the, let's say, acceptance rate, generally how you're doing individual steps of the process was the candidate satisfaction rates. For instance, we had quite Like a satisfaction rate, I think, was way lower, about 60%, like three years ago. and I was at 82%, which is good; there's definitely some room to grow, sorry, and I think one of those reasons is because of the transparency we give to the candidates now more often about what they can expect in a given role, but there's a lot of things that you can definitely measure throughout the process. and myself being a data person, I definitely would say, Okay, we can optimize at this stage; there is more optimization that can be done at the offer stage, so what we do is we also discuss once per quarter with our tech recruitment partners and then just look over some data, so how many people did we hire in the end, what's the acceptance rate, what's the process, how long did it take, and then try to make some optimizations on the basis of that and get more people involved or cut down on some things as well. So there's always constant learning and constant optimization involved, okay? We

TIM: Yeah, it's interesting the way you've taken your data hat and applied it to hiring, and I've heard similar stories from a few people in different companies who've taken it on their own to look at their hiring funnel because it's basically a funnel like any other marketing funnel. Do that kind of conversion rate optimization you mentioned. I feel like as I hear these stories, it's normally data and tech people applying what is almost second nature to them to hiring, and if I think about all the talent acquisition and HR teams around the world, a lot of the people working in there don't necessarily come from a data or technical background. On average, the vast majority wouldn't. Do you feel like sometimes there might be a lack of data skills in talent and HR teams that would allow them to do this kind of analysis almost like a second nature in a way that it isn't at the moment?

ARTUR: I think there's something you can definitely collaborate with them on, right? It's not necessarily that everybody should have the same set of skills, but as you are the hiring manager asking those kinds of questions and maybe guiding them, it would be interesting to look into, right? like what would help me to optimize that particular aspect of hiring, so I think it's a common sparring, so to say. It's generally like a common brainstorming. Did it go through together? Try to see what we can do best because two parties that are interested and you don't really act alone as well.

TIM: What about hiring heroes? Is there anyone who you can think of throughout your career who you learned a lot about hiring from? And if not, is there anyone? What would a hiring hero look like? What would this kind of mythical superhero character who does hiring in an amazing way be?

ARTUR: Yeah, that's a really interesting question. I think definitely one of the hiring heroes was in my current company as one talent acquisition partner, like Demis Florian, and I think he'd been interviewing for so many different positions, generally like in tech, and we have partner management and other different departments, and I think what I can definitely see is this building up a lot of this context around the rules and expectations, like it was in so many different departments because it was not really focusing on particularly one, let's say, given function, but there are multiple of them, and definitely to get a lot of people and bring a lot of people on board. And I think what the hiring hero would have is that perhaps I want to say that they are really good communicators, right? They really can get to people; they can really get just good chat with them, understanding exactly what they need and what we can offer, like also establishing the trust and transparency at the same time. and most importantly recognizing their own biases as well during the interview process. I think that is also really important that we often overlook at the same time, so these are the people who really are committed, who can establish a really good relationship with the candidate, who can really outline the process exactly fully. So, and so they have trust, and what can they expect? I think that what actually matters was the candidates.

TIM: Is there anything else you'd like to chat about?

ARTUR: The hiring is complicated. I think we have also to adapt all of the time to the market, to the conditions, to the candidates, that our skills never stop learning as well as how we use technology and how we get to learn people trying to paint a really accurate picture to the candidates we're interviewing with. and generally now is good that there's many people more people involved so in the process itself, so it was really, of course, you make mistakes; we all make mistakes, and we shouldn't be really transparent about them as well, but by having your team of talented position partners, the other people who I you from your department as well I think you try to minimize as much as possible hiring a different bad candidate as an example; it doesn't really fit your organization, which is great because you're not really alone within that tedious process of hiring, and it definitely can take a lot of effort from you as an individual.

TIM: fantastic