Episode 124

Felix Sander on Unveiling Motivation in Interviews by asking Real-World Scenarios

TIM: We are live on the Objective Hiring Show. Today we have Felix. Felix, thank you so much for joining us.

FELIX: Thank you very much for the invitation, Tim.

TIM: It is absolutely our pleasure to have you on the show, and where I normally like to start the episode is just to hear a little bit about our guests just so our audience can start to build up a picture of who they're listening to today.

FELIX: Yes. It's a pleasure. So my name is Felix Sander. I'm a director of business intelligence, data infrastructure, and finance for WOW games. WoW games, a video gaming company from Hamburg. And we are a hundred percent subsidiary of the Azarian group, which is very active in the edtech market. I've now been with WoW Games for three years. But before that, I've also been working in the field of data science as a team lead for Xing Yi recruiting. It's like the German LinkedIn sometimes being referred to. So there for four years and before that. I was also working for another video game company, also in the field of data, for more than five years. So overall, speak about like 12 or 13 years of data in the industry.

TIM: Nice, nice intro, and the first question I have actually is, do you play games yourself? Do you get into these games?

FELIX: Yes, there's a difference in whether I would play this if I wouldn't work for the company or if it's like for market research purposes. So some games are definitely a no, but yes, I also, since a kid, my first console was the. Atari 2600, then Super Nintendo 64, everything. Like, I still have the switch at home; play on the PC at work. We have a PS5. So yeah, gaming is getting less with family, but still it's there and always has been.

TIM: And of your company's games, is there one that you particularly like and play a little bit?

FELIX: I think from all the games I've worked on, I'm not working for that company anymore. But what I think I've played the most in private was Drakensang Online. I've heard of it because it's, yeah, it's known as an action role-playing game. I like these types of genres. I'm a big fan of Diablo 2 as well. I really loved Diablo 2 Resurrected when it came out. Yeah, these types of games I also. Like to play in person, but now we are also very active in this field; it's called pure social casino. So you have the slots and these types of games, which I'm also like, also from a professional perspective playing, but I see the fun in it.

TIM: Is it one of those? I wonder if it's working for a gaming company; can you get away with playing games at work? Is there, oh, I'm just doing some user testing and doing some research and trying to figure out the tracking?

FELIX: You're getting in trouble if you're not doing it.

TIM: Okay.

FELIX: So if you have too little experience, and especially if you're working for the product team, and yeah, you are, you're not playing the product, you don't know what's important for the player, what their motivation is, and you do not understand it. And also, you're not looking at the data. How does the user behave then? Yeah, it's actually, so we encourage gaming a lot.

TIM: Yes, and that is actually a perfect segue into a discussion around candidates. And maybe some mistakes that they make in the hiring process and maybe in work in general, because I feel like particularly in the data profession, there's maybe a kind of set of candidates who are almost sometimes a little bit dismissive of the domain, almost not interested in the actual users or the money or the domain or the problem of the customer, and then maybe slightly fixated on just the technical aspects of doing the analytics and data science, and it's surely to their disadvantage as a candidate to have that mindset because they're unable to really be as valuable to the company as they could be if they had more empathy. If you've seen that yourself,

FELIX: Yes, a lot. And basically this is also one of the KO criteria. For me, I do not expect to know. The full company history, especially if you are in the first round. Yeah. You don't know if it's the right fitting candidate and so on. You just basically have seen the CV, but to have a general understanding of, okay, what are they actually selling? What's the value they're bringing to their customer? What kind of products do they have? That's the kind of minimum interest I would expect from every candidate. And I've seen, like, very talented, and on paper, they have been really good. They're working for big companies, well known. And when asked, Hey, what do you know about the company? And then, oh, you're doing this and that. Oh, no, we are actually doing something completely different. And yeah, you could basically say it also happened that I stopped the interview at those points because I also usually have a lot of questions about, Hey, how would you approach this situation? And so on. And if they even know what you're selling, then you cannot have this kind of conversation.

TIM: I was suddenly reminded, actually, of myself. And one of my first jobs was as a commercial analyst with a business here in Australia. That's one of the two large car parks. Two large car parking companies. It's a bit of a job. At the start of my career, I was probably especially introverted and especially interested in the spreadsheet and the data and not necessarily other things. And so I can remember one of the CEOs of this company; there were joint CEOs, which is unusual, but there were brothers. And he was talking to my manager about me, and I think his perception might have been I was just some nerd in front of the computer and not really getting involved enough. This is like within my first week, and he yelled, Has he been into the car parks yet? And she said, no, he's doing that next week. And so one of the first things they got me to do was just to go into. 10, 15 different car parks, which sounds pretty boring and tedious, but it's amazing how much you'll learn because I suddenly learned that some car parks have license plate recognition and others don't. Some, this is 10 years ago now, some still had a manual cashier. So a person you paid the money to. Some had mainly business contracts, people who are paying monthly as part of their business plan. That was most of the car park. Others were casual people coming in maybe for a few hours each day. And then once I started to see this, then the numbers made a lot more sense because I was looking at these reports, looking at the dialogue. Okay, now I get the difference between a casual and a perm, and then I could think forward to then how to forecast things. And it made it so much clearer. And it's not something I would have voluntarily done myself because it just sounds a bit tedious and boring, but it was such an important lesson to really get that domain knowledge to be able to understand the context.

FELIX: Yeah, I totally get your previous CEO, and actually, we do something similar. If you started at our company and you are in a role where you relate to the product, or especially as a data analyst, you just have to play the game for at least five days and write the report about, Hey, what's your first-time user experience? and so on. And afterwards. You're being introduced to all the dashboards and all the data we have, because, as you mentioned, that makes much, much more sense than afterwards.

TIM: Yes. And coming to think of it now, pretty much everyone who we've hired into our company as part of the hiring process, as you can imagine, used our product to do a skills assessment and interview. So that was always a nice thing, that at least we know they've used our product once as a candidate once they come into the interview, and suddenly they start to see what we're trying to do and the point of it, which is, I think, a nice thing we've managed to do. What about when it comes to the interview then? So once the candidates are actually in the interview, some obviously haven't done their research because they might not even know what your company does. That's a pretty

FELIX: Yeah, but then it usually ends quite fast. So yeah,

TIM: But are there other more common mistakes that you would see candidates make or other common reasons why they wouldn't succeed in the interview?

FELIX: The first thing is, yeah, getting to know the product and the company at least a bit because then you can talk about practical exercises. And then, of course, here your skill set plays a big role. How do you approach a problem? How would you, what can you make out of this data? For example, in an interview, we also often generate fake data, which is really good fake data for the interview. It's also not so easy. I spent a couple of days creating a meaningful data site, which is not too large but also very good for the company. Doesn't include anything because we also have GDPR and stuff like that, which is good that we have it, but it just makes it, like, creating a realistic data set for this purpose. Very hard, but yeah, we have these kinds of questions then. And yeah, it depends a bit on the position at the seniority level, but we either give them the data set in advance. And just ask open questions like, What can you make out of this? Or I just hand them over a piece of paper with the beginning of the table and say, Hey, what do you see? Of course, documentation is missing as always, but when you think about our products, what do you think you see? What can you calculate out of these things? And what kind of business questions can you answer, and if they get disconnected, really, yeah. Show that they are able to answer, to generate insights, and to answer questions from the state of, because that's, in the end, what matters. And no one, of course, needs it as well. Hey, how many daily active users did we have to get? How was the trend? And so on. But if you do not understand the why behind it, you're basically a person that does create dashboards, but not. Helping the business to grow, which is, in the end, something I think every data analyst should strive for.

TIM: curiosity in actually understanding the why behind the data. Is that part of it?

FELIX: Or basically, I often refer to it as business literacy. Because you can have the hard skill set and you can be a very good data scientist, be an expert in SQL, R, Python, visualization, Tableau, R Shiny, and what else is there in the market? Just to name a few, but if you are perfect and all these kinds of things, but you are not able. To communicate to your stakeholders, understand what they really need from you, support them in making decisions, and do the right things, and then, yeah, that is something. And also this product knowledge we've talked about, I'm often referring to this as business literacy. If I see that a candidate doesn't have this or I have the feeling that they can achieve this, then it's usually a KO criteria for me. Okay.

TIM: and do Titanic modeling or something, right? Is there just, they're not even taught this, or is it more there's just some weird mindset where they just, they have the wrong perception about what their job really is? Like, your job is not to make a dashboard. It's to help us make money. Like, where does the gut come from? That's what I'm trying to ask.

FELIX: Maybe both, and I'd even add a third one because it's also part of the fun. If you're going in that direction, yeah, I see amazing dashboards in Tableau Public and so on. And even I'm sometimes sad on my own that I have less time or am not creating dashboards at all anymore in my role. When I have the time and. Taking over an ad hoc task, then it's really fun to do that. And if you like creating requirements and discussing these dashboards, which were your stakeholders that maybe do not have the same? Level of data literacy as the data analyst, because in Germany, you need, I don't know, a year to get a driving license. But your stakeholder should get your dashboard or your insights or your analysis directly after they've seen it for the first time. And if they're not, like, not working as a team for multiple months or even years, you do not have this understanding yet. And that is, that's a tough job to do to explain this and basically get your. Get your stakeholders on eye level to talk about this dashboard about the data, about the insights, to make the most meaningful out of it. And also to one of the items you've mentioned about the focus on the learning part. They have very good, yeah. Courses you can take on the internet, awesome YouTube videos, and a lot of libraries you can use. But to be honest, if I think about it, I don't know if I've seen a course teaching business literacy. Maybe I think there's this one book I really like, like this Head First Data Analysis, where they all say, Hey, they really start in the book. Try to understand what your CEO or your stakeholder really wants from you. I really liked that a lot. But that is something that I think is being overlooked quite often. It's maybe not focused, and it's maybe not so fun. If you're really someone that likes to program, likes to create dashboards, and so on, that's your not, maybe not the person for that, but at some point it's part of the job, but it's an important part of

TIM: What I was saying was the last business I worked for, this is now six or seven years ago, had a Udemy business subscription. So everyone in the company could access basically any Udemy course. And I got really into the Udemy courses. And I remember seeing the dashboard that they shared with us. And I was like the leader in the company by about five times. Like I'd done way, way more than anyone else. But how much of that did I really learn and retain? And so much of it was like different technical skills, Python, this machine learning SQL, I don't know, UX. Like, it was the full stack of different things, but really, I'm not sure any of that was going to, at the margin, add a lot to my career skills or add a lot to my salary. Because I clearly had deficits in other places that were maybe not as obviously easily fixable moves, softer skills, and stakeholder management, which are the stuff that doesn't give you maybe the dopamine hit of just doing another Python course, but it's actually really important. Is it a mindset shift then that we would need or candidates, people with. A skill shortage on the soft side needs to think just like you need to focus on this stuff, even though it's hard and difficult and maybe subjective, like you're going to do yourself a lot more in your career by focusing on those things.

FELIX: For me, it's definitely a big part to fulfill this role. Very good. If you want to advance and have a strong career in the field of analytics, you need to learn this. And if you think, Oh, I don't like to do this. It's not part of me. Then you maybe should think about taking a different career path. If it's more okay, I like to program in Python, so a programming career is more, maybe more suitable to you. And yeah, especially discussing the requirements. Hey, what do you really want from me? How can I support you? Is this what I wanted? And then you do the dashboard, and then you get the first change requests because we all get them. Hey, why was this not part of the initial requirement? Was I missing something? Did I not fully understand your requirements and so on? And yeah, I said, this is a big part of the job. And if you're really, if you're a really good data analyst, you're also very good at that part. Silence.

TIM: Quite a lot. If you introduce someone and say, Hey, what's your name? What do you do? Would be the probably the second question to say, I am an ex, I am a whatever accounts, and I'm a software engineer. And I wonder if this is going to be a real problem because if you are a software engineer, you're a coder. And so I feel like a lot of engineers, a lot of data engineers would have their identity wrapped up in actually literally writing the code rather than delivering business value. Again, do we need some kind of mindset shift to not focus on how you're doing it, but more like the value of what you're providing?

FELIX: We encourage everyone in the company to use these kinds of technologies because if you are not, you're falling behind. So if you do not already have this mindset shift, you are already behind. And yeah, your competitors will stomp you to the ground. And also, I think it's a great technology. We are currently learning how to use it, and yeah, I see it everywhere. Also, you can have, let's say, if you have a very small business intelligence team, maybe just one person, it's basically your very cheap companion who can help you to give you feedback on your queries. Oh, do I double the data in my SQL? Can you please double-check that for me? Because if you do not know how to ask the right questions, if you do not know what you want to achieve, if you cannot double-check. The code, for example, in Python, if you're not able to double-check, will definitely give you some wrong scripts. It's a great technology, but still, you have to adopt it a lot. But for example. The last use case I had was to create something. I also tried out ChatGPT, but I think the same works also with the other large language models. It took me like four hours to create the final result. But I think if I had to write the script on my own, it would have taken me a week, and I would have needed to ask at least two colleagues who are better at math than I am. Because there were parts of the script where I was like, Oh, that's something maybe I need to go back to my high school books and read about it again to be able to write the script. So it's a great companion for me to have. I think it can increase the seniority of someone, let's say, who's on a mid-level to make him even more comparable to a senior level because they just give you ideas. They often do not give you the perfect solution from scratch. So you also have to work with your virtual colleague. But it's definitely, yeah, it became a must to work with. Yeah. Yeah. Yeah.

TIM: Technology where you're almost only bounded by your imagination and how you can use it to

FELIX: Yeah. But not only a coder, also the artist. You can be a very great artist and so on. And with how's it called, the one that is on Discord, which is for creating the graphics mid-journey, I think it's called, we are also using this, and here are very talented artists, and their results are not better because they have always been very good, but they just have a higher output and a higher frequency. And so it's not always because, same as a coder, if you're already a good coder and you can already achieve what you want to do and deliver very good resources, you can just speed up your process.

TIM: Are there any bits of your day-to-day life where you found yourself using Claude or Chachi Petit to solve everyday problems?

FELIX: Maybe not every day problems because I'm usually in the meetings where we discuss the problems, get everything on the table, and make the decision. But yeah, it's more like, Oh, I really like to do bullet points. And as it's a language model and it's made for talking, if I need the proper text out of my bullet points, that would be like a day-to-day thing. Or am I not a big fan of writing business cards? Sorry for everyone that ever received a business card from me. Hey, congratulations, Merry Christmas, and so on. Try to give them a personal touch, but these are like the. The day-to-day problems, I have to admit that I'm also using these kinds of models, and it's, but it's fun to work with because they give you also some nice ideas I wouldn't have been thinking about.

TIM: One thing I've been using ChatGPT a lot for recently is actually language learning. It's a really great shooter. I'm learning a bit of Italian at the moment. And again, it's almost like you're just limited by your imagination. I could get it to. In the video mode, just have a look at the page that I'm learning and quickly quiz me on some of the words in Italian, English, Spanish, and whatever. Even coming up with mnemonics for words, like ways to remember words. It's almost unbounded, and it's free, which is staggering. I think this is going to have so many amazing applications. Including probably in hiring, I hope in the next couple of years, some kind of AI-based hiring. Have you started to use any kind of large language model or other type of AI in recruitment? Have you seen candidates use them on the other side?

FELIX: Actually, I didn't check. That's a good point. I haven't checked if the candidates use it. But I'd be totally fine with it because, in the end, how they behave in the interview, how they approach the problems, and so on during the process wouldn't change my perception of the candidates and just having an AI help them on the CV. In contrast, I can encourage it to do so, and I wouldn't. Yeah, I would think it's a good thing because why not? CVs are usually a type of standardized thing. You can make them look nice, and it's even nicer for me to read. And as an HR department, I think it's one thing for the people managers, for example, when it comes to the job description. A large language model can help you just to write a nicer job description that is more on point and makes it easier for you to find the right candidate. And I still remember it like five, six years ago; you were usually going to the job boards, asking for the similar position. Then, Oh yeah, that's the kind of job description I really like. I just copy a bit here and there, and then you're fine-tuning and so on. But that took a couple of hours. So you like for a proper job description, it takes a while to do, and now you basically, because that's in the end, what the language model is doing. They have this stuff crawled for you, so you do not have to look it up on your own, and then you can just fine-tune it a bit here and there, so that's, I think, a very good use case for the HR department, but then, yeah, I, as I've worked for the companies that do the recruiting tools, and they are basically providing services where, in the background, you also have an AI running. So even if you don't know or not what's behind the back, there is definitely something running. And for example, if a recruiter. Wants to approach a candidate via a job platform; they get this type of help. Hey, they're screening the candidate's CVs, they're checking the location, and so on. And then I can approach you, Hey, Tim. Good afternoon because you're in a different time zone, and the machine knows that, and I hope you had a sunny day yesterday because the weather information is public information. So the machine does that. So you can, it's basically a creativity boost also in the conversation starters for the recruiters where these types of tools help.

TIM: I feel like one of the big upsides could be to make hiring more objective and less biased, especially at the screening stage, because I've seen numerous different studies from different countries. That has been shown by applying en masse to thousands of different roles with resumes where they just changed the name. So in Australia, they did one of Anglo Saxon versus Chinese names. I think I've seen something in Germany with, like, Turkish versus German names and call-back rates. And they've shown that there is just rife discrimination with certain groups in different countries. And I think, in theory, a good AI large language model could do that. resume versus job description matching in a more consistent, objective way. So I'm really excited for that. But is there any kind of downsides you're thinking about any risks when it comes to using AI that

FELIX: If you have, I think it's a really valid point, and for me, that's also one of the struggles. I personally also had it in the process, this unconscious bias, I think it's called, because even if you do not want it, it happens; it just happens. It's natural if a person looks similar to you, if they have a name, which is not so hard to read, and so on; you just do it. And for example, one of the things I do is I check the resumes twice, which I want to sort out, and I only compare, for example, the skills. I try to avoid looking at the picture, at the name, and so on. And then you sometimes also, yeah, feel what is called a tuft that you feel, yeah. I don't know if it's trapped, but yeah. That you made a decision that you initially wanted to sort out this candidate, but in the end you were just like, why? Bye. This, maybe it was just because of the name, something which brought to your attention and your mind in the end, you don't know, but I think this is a very good use case. But then on the other hand, it also depends on how the model is being trained, because if the model does the same mistakes as humans do, then it doesn't help you at all. And so also in the model itself, you can have this bias because, in the end, it is being trained from the data that was generated by the humans before. And it's a human thing. We try to avoid that. Some people do it more, some less, but yeah, that, but that, I think it would be a great fix if you are able to have such a tool.

TIM: Yes. Yes. And the consistency, I think, could be a big improvement. And I am reminded of an experiment we conducted a couple of years ago, actually, where we wanted to understand the process of resume screening and what we thought was going to be some inconsistency. And so we hired, I think it was seven different recruiters. And gave all of them 500 resumes for an opening with our company. And we also gave them the job description for that role. And we said to seven of them independently; they didn't know about each other. We said, Hey, like, we're too busy. Can you just shortlist resumes for this role that we've gotten so we can choose the ones to interview? And so we gave that to them. Behind the scenes, though, that we didn't share with them, we actually had all of those candidates test scores on our platform, a skills evaluation customized for that role. And it was fascinating to see a couple of things. So one was, the recruiters all came back with completely different shortlists. I think there was only one candidate who got selected or shortlisted by more than two recruiters. It was just so random. Some recruiters selected three people; some selected 50. It was just such a hodgepodge.

FELIX: Oh, 50. That's a short list.

TIM: And then also there was almost like a negative correlation between the actual skills test performance and whether or not they were going to be shortlisted on the resume. So some of the people who scored best didn't get shortlisted by anyone. And so it was a real eye-opener for us to really see how inconsistent it is. And, for example, if you did some experiment where you applied for the same job 10 times. I reckon you'd probably be shortlisted once and rejected nine times, or if someone else did it, you might be shortlisted three times and rejected seven times. It's just, it's so unpredictable because it's just down to who was doing the screening and how long they spent on it. And I don't know about you, but if I've just manually reviewed 200 resumes, the 200th, I'm going to be pretty bored by the 200th. like, I

FELIX: I have to admit also, with the technology from the past, let's say five to 10 years, how have the applicant tracking systems evolved over time that you can standardize CVs and so on? At least helping me a lot, like, for example, yeah, there are some applicant tracking systems I've used. I've been working together with them as colleagues, so various examples, prescreen is one of the products I've been working with. Yeah, it gets much, much easier. So, for example, it's not like you have a big pile of CVs that were sent over via post, and you have to manually screen them and so on. I wouldn't say it's fundamentally broken, but yeah, there's always room for optimization, but it just got much better already. Okay.

TIM: And say, Oh, this is what I thought. What do you think, Felix?

FELIX: This is my suggestion. I have had some tests before also for other roles I was applying for and so on, or I was even starting, and they did some tests before and so on, but I think the stuff we have stopped before is more meaningful. Because I think an important part is often being forgotten, and then your skill set doesn't even matter that much.

FELIX: And that's for me, the question is, do I want to sit next to this person for eight, nine hours? And I think in AI, I don't know if it can help me with that. But for me, I usually think about three things when looking for a candidate, or I'm checking for a candidate, and that's the first thing, like, when you get this person, can I really do—I want to sit? For eight or nine hours next to the sky and for Timia's first perception, so past, I think we can have a nice chat because you also have this, or I have a problem in daily business, or yeah, I'm in a very good mood or not so good moods today. You are just shouting something over the table. Hey, do you know, do you remember how to do this? Do you remember where this table is and so on? And it's usually these kinds of discussions that make your work life just more fun. And if you do not have this, like I do, I have colleagues who are, I would say, interested people, and one likes cycling a lot, the other one chess and board games, and this kind of stuff. One is an ice hockey coach and so on. It's just when you enter the office, and it's like, Hey, how's it going? How did your team make it on the weekend? And so on. It's just that it's also a big part of the work life. It's just personal connection, and then you can be a very good data analyst, but if you just sit there, do your stuff, and not be like part of the stuff, if you're, if the rest is perfect, I think I can still deal with it because necessarily we do not have to be the best friends, but just Yeah, it's maybe harder to work with than with the others, where you can have just a casual chat. So that's the worst. That's not the worst. That's the first thing. The second thing is, of course, do I think this person is able to do the job? And that's again, we have tested this where this assessment is where the AI can help. Maybe I do not have to put the work into creating the fake data set because I can rely on scores. I can rely on tests. But also, I think for me, for example, if you have Tableau Public or you have a public GitHub account, like the last developer we hired, he has a public GitHub account. And then also in the interview, you can discuss these hobby projects, but it's really nice to discuss because then you also see the skills and also where this person is really motivated to do it. And the last thing is then, and I think this is very hard to teach someone, is really the motivation, and it's someone really burning for it for this kind of job. And I think you, you cannot teach this. This person can be really nice, but I think I've read this also on LinkedIn somewhere; someone wrote it's if someone needs three meetings. to start to work on the solution for a problem, then there is an underlying issue. And that's something I would also look out for, especially like in the probation period, because usually it's like, Hey, we need to solve this. Have you seen this? And yeah, I'm on it. And in the next couple of days or even hours, you will get an update. Hey, this is what I found. Now this is, I think, what we need to do. This is what we can check next. Or for me, it's even more important than the skill set itself. Because if someone already has the skill set, but it's really missing this third part of the motivation of the fire to solve something. Because if they're lacking the skills, they will teach themselves or even just ask the last language model. Hey, how do I do this? And maybe be wrong, but then if you have a good people manager, they will find the issue and say, Yeah, great idea. It's unfortunately wrong, but let's fix it together. And then we have the nice result.

TIM: Yes. But if they don't have, as you said, the fire in their belly, then, oh my God, how can you fix that? That's completely on them to find their own motivation. I guess the question is how then. Do you uncover that in the interview? How would you uncover particularly motivated or demotivated people in that interview process?

FELIX: Usually I try to ask, yeah, business questions that are very practical. And then we had it before with the product knowledge and the company knowledge, which is basically the foundation to be able to have this kind of discussion. So I may think if you approach them with. Real-life problems like, okay, this is what we did. This is the situation. How would you approach this? How do you deal with that? How do you come up with a solution, and then you get into this kind of conversation? And then you also ask them questions like, Hey, now your stakeholders accuse you of having the wrong analysis, the wrong data, the stuff that also happens in real life. Like, what do you do then? And then, yeah, with those kinds of questions about really stuff that can happen on a day-to-day basis. Also sometimes not like the positive things, but also the criticism you have to work with. That, that's always a good discussion, which you can also have about the motivation. Okay. Just trying to look for someone who's generally hardworking and doing a lot of stuff? So I don't know; they're doing a triathlon on the weekend. They've got, they're raising some kids, and they're involved in their local church. I don't know, whatever.

TIM: If they just seem like they get shit done, would that correlate, do you think, to generally being motivated at work or

FELIX: Not necessarily. I've seen people training for an Ironman, and I've seen them. It's a really great achievement. I have tons of respect for that. However, also these people, they have to be some kind of efficient. Because it doesn't help me if someone is just working long hours, but then maybe this person needs training and is focusing on the right things. Maybe this person is overwhelmed by stakeholders asking them tons and tons of questions, because, for example, we had this: having proper requirements is tough. And if you didn't do this before, then you maybe have to constantly rework what you were trying to achieve. And then, there are things that can lead to working long, working overtime, and so on, which wouldn't be needed if you would just focus on the right thing, understand the stakeholders issue properly, have business literacy, and have product understanding, which could be like, Yeah. Reduce your working time to the normal amount and achieve the same things or even more.

TIM: We've covered off quite a lot of ground already today, and I've asked you a lot of questions. If you had the opportunity to ask our next guest on this show a question about hiring, what would you choose to ask them?

FELIX: Yeah, that's good. Good question. I think. At the moment, the world is really changing a lot. And we have a difference in politics, but also with AI becoming larger and larger. I would ask them, Is it also in Germany? We have a recession at the moment. We have some economic change. We have more and more elderly people. And during this time, in general, does it make hiring harder or easier, and how do you deal with that? That would be, I think, a discussion I'd love to have with someone.

TIM: That's a great question. And I'll level that at a guest next week. What are your first thoughts about this? If you compare hiring now to, I don't know, the 2021 peak COVID, very low unemployment, lots of free cash, and low interest rates, it's obviously an employer-driven market now. How do you do it?

FELIX: At the moment, at the right moment, it's really for, as an employer, it's becoming easier. Because of the recession, also in the video game industry, we have a market where a lot of companies consolidate and so on. So there's a lot of talent out there at the moment. It's still. To really find the right candidate that fits for you in your position and so on, it's still not easy. But if I compare it to, like, peak times with very low unemployment rates, we do not have the super large unemployment rates at the moment, but it's just, you know, There are also fewer job descriptions out and so on. So it's at least in Germany. I don't know if it's the same in all regions, but at least for Germany, I think it's getting tougher to stand out for potential candidates at the moment. That's for me, the sad truth for everyone that is looking for a job at the moment.

TIM: Yes. And that's a consistent pattern I'm hearing. In North America and Australia as well. And it actually got me thinking about if I were applying for a job now, how would I go about that? I feel like I would not even focus that much at all on LinkedIn or platforms like that. And I would really try to think about my network and how I could get, like, a warm introduction to a company? Is that how you'd approach it as well?

FELIX: I'd say yes, maybe not only, you can still, if you see a job description where you think, Okay, that's just me, still apply for it. You can also be successful without being in the network, but I'd say, especially in these peak times where it was really hard to find someone also, like if you just published to your, to a job board where you like, there's nothing coming in, maybe some CVs that do not. match the requirements at all. That, that's also normal that you get them. But at that time, I think 50 percent of the candidates I hired in the previous company came from someone who knew them and thought they were good. I knew them; they were part of my network and so on. And you would just call them like, Hey, do you want to work together again? And they're like, Yeah, alright, what do you offer me? Is it good? Let's have some fun again. Yeah, that's like having the network, especially if you have some years of experience; that helps. Yeah.

TIM: Yes, 100%. The power of networks. I feel even more powerful now, as you say, in this kind of challenging cabinet market and one where there's lots of applicants with Chachipiti-written resumes that kind of look the same. Being able to leverage your network,

FELIX: But I don't mind if the CVs look the same. To be honest, if I have the system to standardize them anyway, remove the picture and so on, and just focus on the skill set and so on, to not have this kind of bias. So I'd even appreciate it if CVs were standardized.

TIM: Yes. Yes. I would leave listeners with just one tip. I remember a book I read years ago, and the title sticks in my mind. It was dig your well before you're thirsty. In other words, get your network built early. You don't want to suddenly be out of a job and need to build a network from scratch. So the earlier you can do that, the better the plan for the long run. And it will definitely pay off. Yeah. Felix, it's been a great conversation today. We've covered a lot of different ground. We've gone all over the shop, and it's been really fascinating and great to hear your thoughts and insights. So thank you so much for joining us.

FELIX: Thank you for having me.