Episode 107

Dr. Luís Moreira-Matias on AI, Leadership & Hiring: Building Data-Driven Teams in a Changing World

TIM: We are live on the Objective Hiring Show today. We're welcoming you, Luís. Welcome to the show.

DR. LUIS: pleasure to be here. Thank you, Tim.

TIM: it's absolutely our pleasure and where I'd love to start today is just getting a little bit of a background about yourself. Who are we speaking to today?

DR. LUIS: Sure. Hi there, everyone. I'm Luis. I'm originally from Porto, Portugal. And From heart, I'm a software engineer then dwelled to the dark side of AI and machine learning. Okay, so I did a PhD on AI and machine learning, and that was the, marked the start of 16 years of professional career on driving AI and data product development. innovation and fast paced tech environment. So a bit of everything. So I consider myself an all rounder, diverse experience, startups, scale ups, B2B, B2C past finance, logistics energy e commerce companies. And last eight years I've been managing. Multiple teams and large departments running executive functions reporting to C level and focus on scaling business. Okay scaling AI functions on those businesses, especially in high regulated environments such as finance and logistics. And currently I'm working on my own startup idea that we can perhaps talk a little bit here and there if the topics allow. So

TIM: Oh, exciting. Yeah, I'd definitely like to delve into that. And yeah welcome to the dark side. It's good to have you over here in the dark world. And yeah, originally from Porto, not a city I've managed to get to yet. Unfortunately, I hear it's beautiful. Also pretty good football team. Are you a Porto fan?

DR. LUIS: I'm a fan of a team from the city, Porto, but not a fan of FC Porto, I'm a fan of the rivals, FC Porto, Boa Vista, yeah. It used to be also a very good football team, but now we are on downside, but we'll recover.

TIM: but I'd love to start with your startup. That's really exciting. Can you tell us a little bit about it?

DR. LUIS: I can tell a little bit. So I can tell you that, that's In the world today, so AI is the next boundary of capitalism, okay? So we sca we lack resources, physical resources, to keep growing the economies on a pace that keeps our entire financial ecosystem stable. The way to do so is by growing it digitally, okay? Either through mo More automation or either for services that can be offered at a different scale and the different cost base. Okay, so larger scale, lower cost base they are offered today. And of the disruption that Generative AI has brought to the world, especially to the consumer world, but the different awareness of the capabilities of this technology that open up opportunities. Problem is companies many companies that form the backbone of our economies. I'm talking about insurances, telco, banking They leverage on a lot of legacy systems that are difficult to integrate with. At the same time, we have complex processes that require experts that no longer we can keep hiring and training, at least on the desired speed to grow or the economy needs to do itself. So I'll be working on something to help these companies on a resource aware fashion, because if something that the recent world taught us, so with the advance that we all saw with NVIDIA and DeepSeek is bigger, it's not always better. Okay. Make sense?

TIM: Okay. Yes. A very small sneak peek into the future. So it would be having a full up conversation maybe in a few months to see what you've ended up launching. And I'm excited to follow your progress.

DR. LUIS: I would love to come back to talk, tell you more about by then, but for now. So sneak peek will be

TIM: Yes, a sneak peek it will be. And for now, a bit more about hiring. And if your business ends up being successful, I'm sure you'll be doing a lot of hiring and building out your amazing team. And you've got a lot of experience in that space. Already. One particular thing I'd love to ask you about today is a trend I've noticed over the past, maybe, I don't know, three, four, five years, maybe as data and analytics and data science has grown so quickly maybe companies have not always had senior analytics talents to put in those roles. Senior leadership roles in analytics. So they've sometimes imported someone maybe from marketing, maybe from sales from some kind of adjacent space. And so here you go, you've got general leadership skills. You're now leading up this analytics function. These people not necessarily data people, though. What do you think of this approach that some companies have taken? Does it have some merits? Do you feel like sometimes it also doesn't work out so well?

DR. LUIS: So let's start, if we talk about role of individual contributors on the data, contributors on the data space, obviously the world is changing. So if you will be a data scientist 15 years ago, you will be using R. Today, you are using Python in 15 years from now, probably we use using something else. The same comes with these new technologies with AI. Nowadays, it's possible, for instance, to write queries, AI assisted SQL queries with a very high accuracy. And I believe that experienced Not only in one side, world's one opportunity to be to for experiences, data scientists and data analysts to be more productive on their roles, but also lower somehow have to recognize the entry barrier for people that are non specialized to come become a junior or a mid level person on the data realm. However, when it comes to leadership, I don't so much think that this is the same thing. If you were not never, let's say, in the trenches being an individual contributor on the data space, I think it is very hard to lead effectively. a team a team in this. First, because the area is very technical. Okay, so many people would say you have to have the vision end to end, you have to connect business with technical part. I think first is the area is very technical, so the talent that you want to keep Okay, that one that you really want to keep will be searching for people that they can grow with, they can learn from. Those people will not come, will not be a part time manager or part time leader or wannabe. That is where professional leadership will make a difference. Then, second, of course, connecting, being able to have this vision end to end, which is essential in data, okay? In data you need to get it, it's very easy to get it wrong, it's very hard to get it right. You really need to be end to end. in order to make an impact on your business. And it's very hard. You'll be able to do that coming from non technical functions. I'm not saying not non technical background, but from non technical functions straight to leadership position. So these two factors, I believe make a strong barrier, not for people to assume functions because we see that in companies every day, but for them to be actually successful.

TIM: And just thinking about it now, if someone were in that position, they find themselves suddenly leading an analytics function. And, they're almost of the mindset that a spreadsheet is making their eyes hurt. They're not really a data person at heart. How would they make that role successful? Would it be about making sure that people underneath them are absolutely amazing, and getting the most out of the technical skills of the team that they do have?

DR. LUIS: I'm a lot of fan. So you have two. Many people say there are a lot of management styles. In my opinion, there are two. Okay, two pyramids. This pyramid and the inverted pyramid. Okay. The extreme of each one here is micromanagement. So you have somebody that is extremely functional, extremely rational, knows exactly what needs to be done, and will micromanage each individual team member. Say, you need to do this, you need to take the wheel, you need to unscrew the driver, okay? It is this person. On the other hand, the other extreme, okay, you'll have a person that understands nothing from what needs to be done, but it is extremely supportive and empathetic. I call it the cheerleader. Okay, so I believe, obviously, if you come These two models, by the way, they are wrong, they are incomplete. You need to be functional and you need to be empathetic. But I believe these leaders coming in, they must come. Typically, they try to fit the shoes of the functional. They put it themselves, a very hierarchized environment with a lot of distance between them and the teams so the teams don't even, sometimes even, artificially creating work so that they're extremely busy and so that's reducing accessibility. This is the protection so the teams do not understand their gaps. Let's make them very ineffective. What I believe they should do is rather the opposite. It's come on a support role, say, I have more experience in the business. I can help you. Removing the others off your way to be effective. What is blocking you? Yeah, so a enabler, a neighbor, a complete who is the servant leader. And when it comes to technical be open and say, yeah, guide me to This is not my expertise, guide me for this. There are many ways to be a leader, okay? A leader doesn't need to be have 10 books inside of his head. Oh no, now I know how to do PRC. Now I know to do management. Now I know to turn off performance. So now I know how to review code and be the best on all of this. I think a leader be showing fragility here and there also becomes human and become somebody that you want to. Work with and that they can trust. Okay. That will be very important to generate good teams where the expert clearly will not be the manager. Okay. I think if they are straightforward with that from the very beginning, they have more chances to success. However, the most seniors of the team may also say, okay, it's fine. It's nice to work with you. It's comfortable to work with you. We have a nice team environment, but I cannot learn from you. Search somebody that they can learn from. This is advice to make to be more close to success as they can be. But do I believe that they are equipped to do it? So I think it's hard. You need also the functional part.

TIM: Yeah, I think that level of like humility, I think would help a lot. I can think of. I managed in the past that I've had if she told me on day one, listen, I don't know anything about what you're doing. I don't know that. I can't help you at all with the analytics. Like I trust you to do that. Here's where I can help you. If she just put her hands up and said that from the get go, it would have built a lot of trust, at least in my eyes. Whereas there was a level of kind of face saving and pretending like she knew and understood something that I knew that she didn't and that. Did not endear her to me anyway, so there was a lack of authenticity there. And yeah bit of honesty goes a long way. I think also in hiring, actually I personally feel like hiring in general could be improved a lot if both sides, the candidate and the company were as transparent, almost like brutally honest with each other as early on in the process. Do you agree, or should there be a bit more of a dance involved? How do you think about transparency in hiring?

DR. LUIS: give you a provocative answer. So I disagree because then if people be brutally honest with each other, can they all run away? Why is that? So why is that? Because they will say, I have no idea what I'm searching for. Most of the companies, when they open the data position, okay, they do not have a clear idea of what they are searching for. The managers don't have a clear idea what are the real needs of the teams. Let's have a job spec in the the drawer, or they draw something based on other and they publish, okay, without knowing that exactly. That profile, okay? And I'm talking here profile, both on the technical part, okay? Also, what challenge this person is doing. What are the strengths and weaknesses of the team on technical side. But also in terms of diversity of the team. Do I need a junior? Do I need a senior? Typically companies are just, Oh, we are scaling or we are backfilling. They just put what they have because it's all the engineers, right? It's all data analysts. But we know that it's not like that. Data is one of those type of teams that even if you go, for instance, to a team of data analysts, to a team of data scientists, and you jump in, you zoom in, and you see these people all have completely different experience and specialization. And then you cannot just go and hire whatever, then it gets frustration. Many times you have these situation where data professionals, both managers, but also ICs move forward in the process. Okay. And then come to the stage when they meet business people and say, Oh, but you are talking about this, but it's essential that this person has. experience in the business. And that people get very frustrated with the candidates that will tell, I am, I, you knew this from the very start because on the TV, it's very clear. I never work on e commerce or I never work on retail. So why we went all the way until the fourth interview for you to discover that? The answer is because the IT manager doesn't know what they're searching for. Okay. Also because it doesn't align with the, Interviewing panel, the one with his counterpart, he didn't do his own work, it's not functional enough. Okay? Being provocative, I don't think so. I think we should have a dance. I think the company should share as little as possible with the candidates, because otherwise they will not hire anybody.

TIM: To go down this train of thought a little bit further, if the hiring manager knew what they were looking for, and they consulted with their other stakeholders, they got, they'd gotten everyone on the same page. This is the profile. We're certain these are the soft skills. These are the technical skills. Here's how it's going to compliment our current team. If they were right, then would you recommend a more transparent approach?

DR. LUIS: Ah, in the ideal world, yes, of course, but we are, so we cannot talk in vacuum, you have to talk on I think what I'm trying to say is not for the company to be transparent, is what I'm trying to say, you need professionals, you need bus drivers to drive the bus. So if you're adding manager, doesn't know what you're searching for, probably first you need to fix that. And then you need to go into into hiring. If you were lucky to have somebody that is that close, of course. Be transparent with candidates. Go. Long, long way on kind of experience and building trust from moment zero, which will contribute to increase the average tenure of data professionals in companies, which in Europe is particularly low. It's, we're talking about 18 months. Then absolutely. But unfortunately the market is as conditioned, which I believe that. A transition to that world will be without major changes will be rather slow.

TIM: What about from the candidate side as well? I wonder if they need to think about what they want. in a role, what company's values are shared with theirs. Do they really need to sit down and think, hang on, like of all the projects I've ever done, of all the roles I've worked in, what were the ones that I liked? Why did I like them? What were the ones I didn't like? Why didn't I like them? Maybe the candidate also needs to think about that before they start hammering the apply button.

DR. LUIS: I was, I'm glad you asked this question because I think also the candidates, if you ask me if they should be radically candid with the company about what they want, I think, no they shouldn't. They need to participate in this. And the reason is similar to the companies. They don't know what they want, or if they may tell something, which typically will be a bump in the salary, okay, or work on something meaningful, right? But then when that translates. Into the reality, they will also come with other requirements such as, Oh, I would to grow somehow. I would like to work on something challenging. I would like to have more flexibility on the workplace. But it's not always these wishes that people actually have. drivers for decision making on where to apply for. So there is a lot of sending CVs everywhere and see what falls and for companies as well. Okay. Get CVs in mass share the minimum possible and see what it lands. So it's a bit, it's a bit blind dating. So if you would like, there is even a Netflix series about that, right? You can date blindly and marry someone. So it's hiding. It's a good metaphor for hiding. It's blind dating. So as of today, I think it would be awesome if the candidates would like plan, project their careers two to five years and be awesome, be prepped on the interview to have actual, an actual career choice and share that with the companies, obviously.

TIM: That takes you a lot of careful forethought and preparation. And I know for a lot of candidates, if you suddenly need a job. You're just hammering the apply because you're under pressure, you've lost that leverage, but maybe if you're in a job that you really you've been there for six months or a year, maybe that's now a nice time to actually sit there and think about, okay, where am I going? What do I want? What am I not liking? And almost be prepared for your next move way before you actually need to make it. Which ties into another topic I'd love to quickly chat to you about, which is the job search process. Everyone I've spoken to pretty much, I'd say in the last three months has said a pretty similar thing around getting inundated with applications for their open roles on LinkedIn and other platforms like that, mostly in America, but also I feel similar pattern in Europe. And so companies are getting all these applications, many of which seem quite irrelevant, creating this filtering screening issue. Candidates are then applying to jobs and saying, Oh my God, there's a thousand other applicants. Are you joking me? And it's in this weird death spiral of AI generated CVs and whatnot. So then I feel like one way around that for candidates at least is to try to leverage your networks, try to get referrals into companies where you can. If you were on the market yourself as a candidate, is that the approach you would take? Or would you still go through official job panel or job portals and apply directly

DR. LUIS: Mixed feelings about that depends on the moment of the market. I've saw moments in the market where the referral would mean you land an interview. Essentially when the power is on the side of the employee. I don't feel that is the market that we are right now in Western world us that I feel right now there are many vacancies. So companies need people. But they don't have the money to pay them. So they want to work to be done out of out of in there. What this means is a lot of vacancies that are not necessarily real and not even with referrals. So you go there because anyway, that on, on post COVID they let go. Many of their recruitment professionals, so they don't have somebody to even to screen from them or to sit with these candidates. And I see more and more, for instance, in start ups and scale ups, the pattern of the first interview is hiring manager immediately. Which says how How the market is. I would say in general, especially for a big company, a referral will help you to put you on the top of the stack, but that really depends on if the vacancy is really, if the company is stable enough to go through the process and to, and otherwise not even the referral will help you because most likely the vacancy will not be filled anyway.

TIM: if the vacancy is real? As in, the role may suddenly be shut because the company's.

DR. LUIS: Yeah, the whole, the company has a need for that person so the adding manager manages to open the vacancy. But then the budget is not really there or the budget was there, but they get an haircut as the year path goes. So then when they start having candidates and filling their pipeline, then they come to later stages of the process. They end up rejecting all the candidates and saying they need to Ah, we need to reformulate the job, but no, we simply don't have the money to make that higher. Today, that on a large year on that region, for instance, on a large amount of the vacancies in data space.

TIM: Really? That's so interesting. And not a metric that I've seen or have noticed, but yeah, that's such, it's such an interesting insight. And this is something you hear like anecdotally or you can see directly in, in the metrics.

DR. LUIS: Yeah, I can see that even on, I can see on more on more leadership positions that they are advertised. And then six months later, they are not the size anymore, but they're also nobody announced to that position on that company. Okay. Or they suddenly get all vacancies that are open for 12 months. Okay. So it's, I think it's a, I find it hard that the company would not find anybody hireable on that period. It's simply that, ah, we don't want that price. I, we don't want to, so it's somebody internally stalling because the money's not really there.

TIM: Wow. That is such a waste of time of candidates effort, isn't it? To apply, even if they're not interviewing,

DR. LUIS: And of the companies I would have, yes.

TIM: That's a dysfunctional. What is a hiring mistake that you have seen data leaders make repeatedly?

DR. LUIS: Hire for a technical superman or superwoman. Okay, so data is very This is something that I did myself. Data is a very technical profession. So you end up thinking, oh, I need to hire somebody that knows his stuff. I focus the entire hiring process on that. Okay, because the HR wants to keep it as short as possible. This is good for career experience. So we focus the hiring process all on that. And then, you end up hiring somebody that nobody wants to work with. Or probably at a scenario level that you don't even need it. Or you do not have a career plan for this person because he's just too senior. You don't have growth, so this person will end up After 18 months because he has nowhere to go on this company

TIM: you haven't, so you see that and that's more common than the opposite, wouldn't it be someone who's lacking the technical skills, but as an amazing people person, I feel like that's, it seems less common.

DR. LUIS: On my teams, That may not my experience what my experience Taught me that may not be that bad as long as there are two things This person comes on the right level Or free in this case comes in on the right level. So there is a clear, so recognition from the company package wise, title wise, whatever is the meritocracy currency that this company had to say, yeah, this person is more junior than others. Then it must be a self awareness. So it's not only everybody recognizes this and this environment is set, but the person itself has to recognize, oh, I have a way to go. And then the third is the person must want, in my perspective, to go through that way to go. Okay, this person must want to grow. This person must want to learn. This is more rare to find. Okay, so this combination of free feeds. Sometimes a lot of juniors that have an idea, Oh, let's go for data. Then they enter the job and say, Oh. It is it. I ran away. So type of thing. Other than they are okay, a good person. They don't understand anything about it. And six months later, they'll still be a good person. They don't understand anything about it. So they, that if a task, so that will end their trust. Because, if you send a task to them, I'll need to then grab a task and redo it anyway, myself. So this creates this balance on the team, but somebody that wants to put the effort to grow, yeah, that can be somebody very valuable to have on team. Yeah,

TIM: You want someone who's adaptable and is willing and able to learn quickly. Surely that's like of paramount importance and you've touched on it there. Do you ever try to directly evaluate this in the hiring process? Is there any way you can interview people or any kind of test you can give them that? Unlocks that adaptability, that growth mindset, that willingness to learn, call it what you like. even Yeah, absolutely. Absolutely. Always a behavioral slot. Or, oh, so this is a must that I bring to every, always have a behavioral slot. The slots with specific star questions. With, I start interviews myself, then calibrate interviewers and So a pool, like all the other more technical slots, where we search for self awareness, where we search for Humble, eager to learn, eagerness to work hard, because these will be ingredients for success.

DR. LUIS: As I have to say, for instance, I've used that to build a 20 strong team. At Standard, I've clearly made a difference on having people that are Some of them brilliant from the technical standpoint, but egoless. Okay. At least don't transport that ego on their day to day. So making the environment a good one to, to work on.

TIM: One thing that I imagine is challenging about that and an issue with interviews in general is we're trying to evaluate. How someone's going to behave based on what they say in an interview, based on the experiences they can quote, based on how well they can tell a story to the interviewer, how well they could explain like their narrative. I wanted to throw a bit of a random one at you. This is just enter my brain. So this is gonna take a little bit of time to explain especially because you're in startups now. So why Combinator, the big startup accelerator program in Silicon Valley. They have this kind of three month program where a lot of it is just it's not like this six hours of classes a day and all this theory. It's just getting the founders into a room. What are you going to do? And then asking them repeatedly. Did you do that or not? Like it's very action oriented. We can talk all the day about how much we want to learn and how much these things are we want to do. But the end of the day, action speak louder than words. Is there any way you, do you think, or you've seen an incorporation of that into the hiring process? No, I, anyone can interview and come up with a good story and tell a good narrative, but I want to see some actual, I want to see you show me some growth. Like last week you, yeah. Hadn't lifted a weight in your life. Now you've lifted some dumbbells, like something in any bit of life that shows that they're heading in the right direction.

DR. LUIS: During the process? No. Typically we want the process to be as fast as possible for many reasons. Candidate experience is one of them. Cost of opportunity, it's another of them. So this is a very small time window for us to get the signals that we need to take decisions. I also have to say that I have to challenge a little bit the idea that anybody can tell a good story. It requires a lot of emotional intelligence to write a good story, to prep it, and to sell it to the interviewer. And if they can do that very well, probably they can also cope on the work environment. However, I agree with you, it's hard to test resilience. On that slot, we still have, it's sad to say, we still have probation period, that serves for that for you to see a person on a, a period of time that you can actually see them on a real work setting how it goes. It's still a small amount of time, but it gives you a little bit of sample. Of course, you have a cost to hire and then the cost to all of the costs. I'm well aware of that, but I would love to have that magic one. So if you have ideas on how to get that type of signal on the hiring process. I'm without making it two months long or three months like Y Combinator makes it. I will have

TIM: magic bullet by any means, but we came up with something that was interesting, at least for one of our roles. So I could run that by you now. Last time we were hiring software engineers, we thought. Basically, what we've been saying now is the single most important thing really is how quickly can they learn to do something they've never done before, because in a startup, as an engineer, that's going to be most days. And what we came up with was, okay, could we give them a little algorithm to build like a take home project kind of thing? Not a particularly complicated one, but one which they had to write in our language. Because. I don't know about you, but not many software engineers would write R. So what we were looking for was a couple of things. One was, oh, would they say, hey I have no interest in doing that. I don't like R. R is stupid. That's a statistical language. Why would I want to learn R? That would be a problem, I think, if they had that attitude. Two would be, if they tried to learn it and they just couldn't they just kept getting stuck. They couldn't install. They couldn't find out where to find the right packages. They just couldn't do it. That would also be a problem. In the end of the people we put through this, I think most managed to actually successfully do and we're interested and engaged in the process and we hired some of those as a result. So that was an interesting way to do it specifically for software engineers, because none of these people knew art to begin with. They managed to teach themselves in a few hours, punched out some code that worked, it shipped a bit of product. Great. If they just did that every day for the next two years, they would be, I think, a pretty good engineer with us. So that was what we did. I don't know how to scale that to other roles. And to your point before, it is a bit of a cost to the candidate. Of course, it's like it's a few hours of effort. It's not nothing. But yeah, what did you think? What do you think of that approach?

DR. LUIS: take home assessment out of comfort zone, so Yes, but it's my answer to to that. So you start by having an assumption that he never did it before. That's a story it will tell no way, how familiar he's with that or not. Second, now that always existed, but as soon as you send the task for the candidate. There is no way to know if he coded himself. He just knows a friend that codes in R and coded for him. Hired somebody in, I don't know, in India or whatever to do that for him. Or simply went to Claude, make a prompt and get the code for you to to do it. These days. But but before the friend. We'll still, the opt in option will still be there. I think I, everybody knows somebody on the high school that paid for homework somewhere and somewhere. Okay. That's the approach before it was or the local network nowadays on is internet power empowered by an AI model that's zipped the entire internet on it, but it's the principle. is still there. And then there's a caveat, which if it is on an area that you should know well, then you can go on a deep dive session that you understand how this, if this person can stand these choices. But if it's not on something that it went it also yeah just the fact that he built something that works is already a signal. So I think in terms of soft skills, I think you are right. If you ask propose something like this to a candidate and they tell you why I will do that. That is not that shows some behavior of way. I'm sure that you detect a lot of those. So that is smart on the technical side. Yeah I see I see caveats, but nothing like experimenting and see how that would work. Yeah. in practice. So I think if they have the emotional intelligence to accept that challenge and to understand what, because if you are experienced, if you are working on C or in Java for 15 years and you accept that, you are smart enough to understand that the person on the other side is not searching for your art skills, but searching for something else. If you have that emotional intelligence. You'll also prepare good stories to answer the behavior the behavioral slot. Yes, but, yeah, I see merit, yeah, on that, but, yeah, some caveats to work on.

TIM: languages that a data analyst, a data scientist, or a software engineer, anyone, is actually going to need to know?

DR. LUIS: think

TIM: using your prompts, and maybe it's specific domain knowledge of your data warehouse

DR. LUIS: You group there a wide range of Let's start with SQL, okay? I believe if you are building a tech first company, everybody in the company Needs to know SQL. I struggle to believe that somebody cannot put together a SQL query even in I would say in HR. A large part of the people I would expect to know SQL. Now the question is, what it means to know SQL? So 20 years ago, that would mean to be able to autonomously query Query For that you need to code yourself. You need to know the syntax. You need to be able to solve the errors yourself. You need to open something like Microsoft SQL Server or equivalent. Today you may do that with a prompt. Okay, so I think that people will need To know SQL, punct on a tech first company, all of them. I think the definition that what it means to know changed. So to know SQL actually to be able to extract and validate knowledge that they pass through a natural language prompts to some sort of agent. I don't have shared the same feelings towards Python perhaps, but towards SQL, yes, absolutely.

TIM: and why your views between Python and SQL different? Could you unpack that a

DR. LUIS: Python for is it's a programming language, right? So I, there, I do not, let's put it this way. Today, there are enough levels of abstraction. So you can do, Pretty much good searches, good summarizations, good reports with SQL, which should cover 99 percent of the use cases. If you have a use case that needs Python, that means somebody that knows what he's doing. He needs to know more than Python. Okay, he needs to know probably about statistics, he needs to know about other things. And then that's for others. That's not for the, in this case, for HR lady, for the person that the marketing expert that it's for, it's a scientist or a data analyst to do, to that's my belief.

TIM: fix the hiring process? If you could,

DR. LUIS: It doesn't give much restrictions there, so what I wanted is, okay, I write a wish list of a person and then a person appears, so that will be my magic wand, so I think you need to be a bit more restrictive with your question. So what can I do with the magic wand? What can I add? Because what I will do, yeah, I'll write what I want for a person. And then I put the magic wand and the person appears starting immediately. So on, on the job. So that is a bit unrealistic. So inside of that world of the, of magic world, what is real and what is not. Can you give me

TIM: Yeah. Okay. So let's confine it to a world you can imagine in the next couple of years. So if you could forecast, if you were not Nostradamus where AI is going or whatever else. What would an ideal, what would an ideal hiring process look like to you in 2027 if everything has gone perfectly? Maybe that's one way to put it.

DR. LUIS: I think you need to be transparent on the packages. Okay, it's a trend that is coming from US. I think this needs to be happened globally. Not only on the packages, but on The perks as well. I need to be able to be more global. Okay. We cannot say this is the salary for the U. S. But this is the salary for Romania. I believe we will need, We didn't invent the globalization, so we live on globalization. That means you do the exports on imports, so eventually, yeah, there will be unified currencies, there will be, so If somebody wants to live in Romania, I do not see, personally, why we should pay this person, if they're doing exactly the same job as somebody in New York City, I don't see why they should be paying less. So I think that is something that I really wish to To see fixing hiring and I see a trend for that, but if I could have a magic wand to accelerate it, I definitely would. Second thing will be some sort of So you need Some sort of regulation for both, for candidates and for companies. So I cannot simply put, so I cannot, I'm a company. I cannot put the I want to hire code monkeys. Okay. Okay. So this, I cannot put, okay. So this is, doesn't, I need to show that this is this is a real deal. It's a vacancy. Gives work conditions to this person, It's not the add off position, that then I'm the only person on the team. Okay it's something that I see a lot, sometimes in vacancies on UK I'm sorry UK, so that, it's an add off position. Salary, 35k but you just see that after you've been interviewed, by the way, you'll be the only one. Add off what? Okay. It must have some sort of regulation for the companies. Okay. And what they advertise that they have, a minimum of. But at the same time also for candidates, okay, that they have a professional experience. It minimally matches what the company is searching for. Okay. That will not be solved with LinkedIn. Where you write whatever. So I think that AI can help a lot. So on, on doing that matching and, but I believe really the third party that regulates these two sides of demand and supply because today it's a nightmare both for companies and for candidates. Go

TIM: Bovistar.

DR. LUIS: Boa Vista, thanks tim, pleasure.