Episode 50

Dan Kaziyev on Objective Hiring Challenges in the Data Job Market

TIM: Dan, welcome to the Objective Hiring Show. Great to have you with us.

DAN: Hi, Tim. Thanks. And happy to be here.

TIM: Uh, Dan, I'd love to start by getting your thoughts on like the current state of the hiring market for data roles, because I've spoken to a lot of people recently who feel like it's broken in many different ways. Do you think it's broken? If so, how is it broken? What for you are the leading problems and challenges at the moment?

DAN: Thank you. Well, I think in general, I would say data labor markets and hiring market is a very dynamic kind of environment, first of all, so there's lots of roles. These roles are changing quite fast. There is a there's a lot of new roles. Some roles maybe are kind of fading away. So it is a very dynamic place, and I think as a participant in that environment, I feel kind of privileged to be there. I think compared to many other professions, actually, like maybe finance, more traditional professions, I would say the kind of data job market is one of the exciting places to be. Yeah. Having said that, uh, Having myself had to kind of look for, for roles, uh, maybe several TIMes throughout my career in the UK, um, and outside of the UK as well, it can be painful, uh, to actually, uh, go through the process as a candidate. Uh, and likewise, having been on the other side as, as a, uh, hiring manager, it was also quite painful actually to, to hire. So, you know, Despite being a dynamic and, you know, a great market compared to other traditional markets, still there is a lot of things that can be improved both on candidates and hiring manager side.

TIM: And I'm interested in then just drilling down in a few areas. So your experience as a candidate, I'd love to hear, you know, almost a sense of the good and the bad and the ugly and did this vary by the market you're in? Has it changed since you've gone up the ranks? Like, has it gotten, for example, a bit easier as you've been more senior? I'd love to get your thoughts there.

DAN: Yeah, absolutely. I think as a candidate, uh, yeah, so I guess it definitely, it gets much easier, uh, as you go, as you, as you build up your CV. Uh, but I think it's not kind of a linear upwards sort of trajectory. Actually, it then goes down after some points in your career. So when you're just starting off, kind of, you don't have any brand names on your CV, it is difficult to to get shortlisted to the first interview. Uh, or even if you happen to have the first interview, You know, if the company name you worked for isn't as recognizable, you won't be prioritized if there is a similar candidate with a more recognizable brand name on their CV. So that definitely helps having experience in a company that people know. And then, but that kind of applies to the hands on roles. I would say once you're kind of an associate level, sort of an analyst level, uh, and then senior analyst level. There, having that experience, uh, and having some brand names helps, and it makes the process faster. But then once you reach a point of sort of head of analytics, uh, actually, it kind of doesn't matter, you know, how much, uh, you know, experience you have. It's just that there isn't that many roles out there, usually. Uh, and just the market, there's just, the market is small. So kind of your experience is quite different. And I imagine when you get to kind of chief data officer level there, it's, you know, there is even fewer roles there. So kind of the, the hiring cycles last even longer than that. So I would say kind of changes or ups and downs. Uh, but definitely I think what helps is having. Kind of something on your CV signaling that, you know, you're, uh, you've been tested and vetted, uh, by rigorous, uh, processes, um, and, and, and people having that stamp, uh, helps a lot. So for me personally, uh, I think having that experience at Uber, so I worked at Uber, uh, as a analytics manager that really opened many doors for me. So after, so getting into Uber was pretty easy. Was not that easy. I got there from consulting But then once I had that badge on my CV that I worked for Uber and later on it really Generated a lot of demand for me as a candidate and it was much easier to then interview But having said that it kind of depends on who you interview with. So if you're interviewing with Large companies like Meta, for example, or Google, regardless of kind of, uh, what your CV says, they will take you through the same process typically. And that process is quite standard, quite rigorous, uh, quite hands on. Uh, so they expect you to do kind of, uh, coding live in front of the interviewer, whether it's SQL, Python, and then problem solve, et cetera. So kind of there, like your background helps you get through the door. But then the process is quite fair, quite predictable, actually, you can prepare for it. But you don't really have an advantage or disadvantage there once you're in. But then when it comes to small organizations, what I found is quite often actually like startups, for example, they wouldn't even know how to interview you. Uh, as a candidate. So they will kind of look at your CV, look at your credentials, and then ask you some common sense questions. And typically you will speak to some executives, and, uh, quite often that does the job. Uh, so that's what I found in, uh, you know, small tech organizations. So the level of kind of rigor in these processes is, You could say shockingly different, uh, depending on the company you're, you're, you're, you're speaking to, uh, but yeah, having that, uh, you know, badge on your CV always helps, uh, to get, to get through the first round.

TIM: You mentioned, yeah, shockingly different or staggeringly different, uh, levels of rigor in the hiring process. So one end of the spectrum, you've got this elongated, several months long, numerous step process and a feign where they kind of put you through the ringer. And then there's maybe the other end of the spectrum where it's, uh, a startup, a good conversation with a founder or someone, someone like that to, to really get, get, you know, Yourself in there. Um, uh, and you mentioned also in passing. SomeTIMes the small companies don't even know what to ask you. Is that because you would be the first data hire? So they kind of, they don't know what they don't know because the person who would be interviewing you would almost be yourself or should be yourself, but you aren't in the company yet. Is that part of their lack of ability to really interview?

DAN: I think that, yeah, but definitely that's part or can be part of kind of the reason why in my case, Actually, there were, there were data scientists in the team that were building this AI chatbots. Uh, but when I was interviewed for the analytics role, uh, some more kind of BI role, uh, for that startup, they just didn't bother to involve them, uh, in the interview process. Although they, they could have tested my SQL ability, you know, just general technical understanding. Uh, they also could have involved some developers maybe just to kind of sense check, like, what Does he understand how ETL works or, you know, uh, like some, some, some basic fundamentals, but that wasn't done either. So I would say it's, it's kind of because maybe someTIMes you can be the first data hire, but also because they haven't built the muscle of hiring people in a rigorous and fair way. regardless of roles. So quite often, startups, and we just kind of have arbitrary processes, unfortunately, for hiring people. Now, with something like ChatGPT actually, you can standardize hiring, you know, and you can be, you know, the only member of your startup of your company and you're hiring the second person. You know nothing about technology, but you can use something like ChatGPT to structure, you know, an interview. That will give you an idea, at least, a much better idea than if you were just to go in there with your gut feeling.

TIM: Yeah, you're right. These technologies are just, um, have the opportunity to reshape hiring drastically. I think very, very quickly. Uh, and I think that's a nice segue onto the other side of the market, which is, uh, broken hiring from the company's perspective. So one thing we've heard, we've heard a lot of in the last few weeks has been, especially from companies in Europe, UK, United Kingdom, sorry, the United States are complaining about being inundated with CVS. They're basically all saying that they're getting heaps of applications. The applications all look the same. Lots of them look really good. They seem to match the job description very well. And there's an uncanny similarity among them. So it seems like there's a lot of ChatGPT usage to create like the perfect CV. Is this something you've noticed, uh, yourself? Um, and have you got any thoughts on, on this problem?

DAN: Actually, in terms of people using Chat GPT for CVs, I haven't encountered that yet, uh, myself, but, yeah, obviously that's, uh, It's a very, uh, easy, uh, win for, for candidates to, to take it through chatGPT and to improve their resumes. What I have noticed though, is that quite often what people claim on, on the resume, uh, doesn't match their actual abilities. Uh, unfortunately, uh, and, and that's why you, you have to take them through, uh, uh, Uh, this fair, rigorous process, and that's why, you know, the FAANG companies actually do all these steps. Uh, I think that there is a reason for that, and it works. Uh, so, kind of what I found in my experience hiring for data roles is you have to hire, I mean, you you you have to take these people through. The basic, uh, you know, interview steps of testing their SQL If that's a kind of data analyst, data scientist role, testing their Python, testing the statistical knowledge, it just has to be done. And unfortunately just can't trust what's written on on on a CV. And that is the most TIMe consuming and difficult process. You have to schedule these interviews with every candidates. They have to be rescheduled quite often. SomeTIMes I don't show up, someTIMes you can't show up. Then when people do show up, they said they have advanced SQL skills, but they can't join two tables together. And kind of, as a hiring manager, it just can be really frustrating. Uh, that maybe out of 20 interviews that I did in the UK for data analyst role. Maybe one or two people actually kind of, you know, met the bar that was described in the job description. Uh, and then you take them to the next stage and out of these two, maybe none of them actually meet the next bar, you know? So, uh, that was, uh, yeah, I mean, it was a full TIMe job for me as a head of data, uh, to interview people basically, uh, for the first six months. Uh, and you could argue that as kind of the first data hire. Uh, what kind of data analytics be I hire in the company? Your TIMe is quite valuable, but unfortunately it has to go, uh, to these kind of, into these basic, uh, checks, uh, that people actually, you know, can they, that people can do what they claim they can do.

TIM: Yeah, and this, this has always been a problem. And I wonder if it's going to get exacerbated by the fact that now candidates are partly outsourcing the writing or opTIMizing of that CV to Chat GPT And so, it's not like they're lying now, no. Chat GPT hallucinated and it's exaggerated. So it's not even my problem, it's, it's, I'm just using whatever it gave me. Like I could easily see how candidates would either do that deliberately, and sort of not worry about it, or even accidentally, because they're so busy trying to apply to jobs, they're not going to scrutinize every little word that Chachapiti has rewritten. So I wonder if this problem is going to get even worse now. What do you, what do you think?

DAN: Yeah, I think it, it might happen. Uh, definitely. I think definitely like as a human being, you sort of judge. You know, uh, quality of a candidate, not just based on the content, but also the way this content is packaged. So I'm sure that Chat GPT will help, you know, create this good packaging where, you know, the, uh, kind of the, uh, the, the, the, the way the experience is described, the words that are used, uh, you know, are quite well selected. They, they, they flow nicely together. Uh, it's easy to read and that will just give you the impression that, okay, this kind of, that seems like a, good kind of, at least they have an ability to put together a concise information, rich CV. Uh, but it turns out maybe it wasn't them. It was Chat GPT who did that because actually being able to condense information into something concise, well structured and, you know, the flows is a very important skill, uh, for, for data roles as well. Uh, so definitely I think it will, it might kind of, uh, increase the, The percentage of Candidates that have passed through the first round but then, yeah, they'll, they'll still in a company with solid hiring processes, they will still have to face, uh, you know, actual technical tests and, uh, kind of, uh, proper, proper interviews. Now, I was just thinking in my mind, like, how, like what was a good metaphor, I guess, for, for hiring people and maybe for hiring for data roles. So. In my previous company, Gelato, there was this metaphor that we are like athletes, we're like a sports team. Uh, and, you know, uh, we all, we're all here to, to show, we're showing up to perform as a team. And if you're not performing today, uh, unfortunately you're going to get substituted. Uh, because, you know, we're here to win. We're not here to, you know, hang out or, you know, to be kind of, uh, a family. We're, we're a sports team. And when you apply that kind of mentality to hiring. It's almost like if you take, uh, soccer or football, uh, as a, as a left wing, uh, football player, I expect you to run a hundred meters in 11 seconds. Uh, and then I, I want you to show me that you can actually do that. Right? And, and maybe you could do it LA last year, but, but if you can't do it anymore, uh, unfortunately you can't cut it to, to the team. So the, these kind of, uh, standards and, and, and tests, I mean, I'm sure they exist. Yes, the medical test and the fitness test that takes place in sports hiring in the same way. I think it should be a norm to do similar tests in data hiring and kind of general hiring as well.

TIM: Yeah, I think that's the way it has to go because as you've mentioned already, if we rely or overly rely on just taking the candidate's word for it, that's doomed to fail. We already know that, like we don't need any more data to understand how crap a CV is as a determinant of who is worth speaking to. Because as you said, you have a conversion rate of like 1 in 20 or 2 in 20. That's my exact experience as well. Yeah. Um, because candidates are going to exaggerate, they're going to lie, they're going to misrepresent things. And that's only on one side of the equation. There's also the other side, which is the candidates who under inflate their achievements. Maybe don't include everything on there. Then there's the whole bias angle of like, who's looking at the CV? How tired were you when you looked at it? Did you spend 5 seconds or 30? So there's like just so many reasons why I feel like that screening step based on a CV is doomed to fail, um, and has failed. Uh, where are we going to go with this then? Because if all these CVs are just, you know, uh, not representative of reality, even if we automatically screen them with AI, for example, which would solve some of the problem, it's still a crap data set. So do we almost need like a new screening tool, like some new data to sit in between the application and the first interview? What do you reckon?

DAN: Yeah, actually, I, I really agree. I think with But, but where were you leading this, uh, conversation, uh, towards. So I think if I was to take a step back, you know, maybe some people exerted lots of effort at high school and they happened to get to a really good university. Uh, so they have, you know, really good brand name on their cv. I mean that deserves, you know, uh, respect. Well done, but it doesn't mean that the person who, you know, in high school didn't exert as much effort, or maybe wasn't in shape of it. For any other reason, but then started, you know, really focusing on, you know, data skills later in life, and maybe they didn't graduate from university or on an unknown, less known university. It doesn't mean that they should have a lower check. It doesn't mean that they're less fit for the specific role you're hiring for, right? Objectively speaking, it's obvious. They actually could be a better fit. But unfortunately, kind of the way things are set up now in the hiring system is you're looking for signals that this person is hardworking and this person has high IQ, et cetera. And the way you do that for junior candidates is you look at the university brand name and you look at if it was, you know, uh, the GPA scores, et cetera. So if we were, if there was a way to remove all of that and just do a fair and square test. Right. So this is a hundred meter sprint. Whoever runs faster kind of gets to the next round. That's it. I don't care if you graduated from Harvard or you know, you never went to school. Uh, if, if, if you can do that, that kind of means that you're ready to, you know, uh, you you, you you met one criterion for, for this role. Uh, and I feel this kind of meritocracy. Is what we really lack in hiring, uh, and, uh, I wish, uh, there was a way to, uh, to in in in kind of install that and hopefully in in an efficient manner as well, in a less laborious manner, uh, because you can't, you know, do the same test for 200 candidates that applied for for your role manually, right? Uh, that that will be a much brighter future in my mind, much more kind of, uh, fair, uh, Uh, and equal and, uh, also more, more meritocratic and a win win, better for companies and better for the candidates.

TIM: Well, yeah, it's probably doesn't surprise you to learn. I agree 100%. I feel like what you've laid out, though, is a vision that's quite at odds with the typical narrative we hear in talent teams where I don't think I've heard the word merit for about 10 years and their reaction to testing and these kind of objective measures is almost the polar opposite. They'd immediately be thinking, Oh, this is biased. This is unfair. You know, this is going to be against this type of candidate or whatever. So it's, it's very interesting. The reaction also generally around automation and AI, just like concerns and fears that it's somehow like anti human or something, but I personally feel like that's misguided because. If the current hiring process is so tediously manual, how humane can it be if it takes you three weeks to get back to a candidate, if candidates can't get any feedback, um, if the best candidate routinely doesn't get hired. Like there's, there's some weird missing thing here where I feel like the solution is actually very obvious, but, but the mentality is not in line with this solution at all.

DAN: yeah, that's a whole different topic, like the fact that most candidates never get a proper response from the employer, and they put all this effort in, and yeah, most, at least from my side, when I was interviewing. Maybe 80 percent of companies I interviewed at never bothered to send me a proper reply. They just kind of ghost me. After two or three rounds of interviews, they would just ghost me, basically. Even big names in the UK, I won't name i won't name them, but kind of big tech names in the UK tech scene. You know, three interviews. That just, they just ghosted me. Uh, no, nothing. Uh, and that's kind of normal. Foreigners just kind of become numb to that. Like, okay, well, that's just how things are. Uh, you know. And I don't think it's okay, actually. I don't think we should accept that.

TIM: We shouldn't end. Again, it's what's frustrating for me is I feel like now the solutions to these problems are not that complicated at all. I mean, if you had an interview and you had the, um, like there's so many AI meeting recorder tools that have been out for years that accurately transcribe what you're doing. Now with a large language model, you could almost start to grade candidates against the questions you're asking them. If not, someone could certainly be writing notes there and you just need to email it to the candidate. Like it's not really that hard. Yeah. But I feel like because the whole hiring process is currently so tediously manual The people managing it have so little TIMe to really spend on anything of value because they're manually doing scheduling manually reading hundreds of cvs You know, it's all this tedious manual crap If we automated away some of it with good technology, then you could actually have a more humane process. You know what I mean?

DAN: Uh, absolutely. I, you know, I would love for someone, um, to actually count up, like, count all the hours that go into interview. It must be an insane number of hours that goes into interviewing, say, just for data roles on both sides, right? So maybe for every job, a kind of match to happen, uh, maybe there is a, I'm sure there is more than 10 hours of interviews. Uh, if not, you know, maybe 20, maybe it's 50. I'm not sure. And so there's lots of duplication. People having, you know, You know, interviews with 20 organizations asking, answering the same questions, right? And same for the companies, them having to interview, you know, lots of people that don't really, obviously they don't meet the mark. Um, so yeah, there is such a huge amount of inefficiency. I imagine all that TIMe was reinvested in something better than, you know, than interviews.

TIM: Yeah, well, maybe that's where the solution will come then some kind of Um, intermediate product that, as you say, maybe validates and asks questions of candidates, a certain set of things that every company's interested in, like communication skills, the basic technical skills for the role, give me an example of X, give me an example of Y, and then you could just connect and share that information with different employers who all want to know the same thing to maybe bypass that first step or first one or two steps. Because as you say, it's so tediously repetitive that if we Automate away some of that that's just yeah There's got to be some big cost or efficiency saving at the end of the day for that kind of stuff.

DAN: I think the value created will be huge. It will be billions and billions of dollars worth of value.

TIM: Yes. And uh again, I feel like we're Like the underlying technology to do that is is there we just need to get on with it personally. Um, uh What about? Bias, so we kind of just touched on it a little bit Uh with the current way hiring is typically done which is like a manual cv screen A CV has all sorts of things on it that are fundamentally irrelevant to choosing the best candidate. For example, their name, which reveals their gender and ethnicity. No one needs to know that. Um, you could argue the hobbies. Some people feel like that's a gray area. They want to know someone's hobbies. For me, I, I would rather not know them because I've personally been biased against candidates because of their hobbies. And I can share some examples there, but there's a lot of stuff on a CV. It's just like, I don't want to know. This is just noise. Um, uh, and what I'm interested in then is how do you see us moving to like a fairer, more objective hiring process that you mentioned that was kind of more merit based? At the moment, it starts in such a bad, on such a bad step. How can we move away from that do you think

DAN: Yeah, I think we definitely can. I think in general human beings are so adaptable and malleable. We can, you know, we can change, I think, quite easily in the long term. No. At the same TIMe, we are kind of stuck in our ways, right? And the way, you know, hiring and CVs have been structured historically, it just kind of has this inertia. I think you're totally right, like, why do I need to put my hobbies, and why do I need to know my name, right? Because it's not that relevant. And maybe in the same way, do you need to really know kind of my high school and kind of my entire life journey? How relevant is that to the specific role I'm going to do at your company? Maybe it has absolutely nothing to do with that, but I think the way people, human brain works, it likes the story, story narrative. So we kind of form a story in our mind about each candidate. Okay, so they started here, then they went there, then they went there. Okay, it looks like it's a good progression. And you start rationalizing in your mind the story. And especially if you can relate to that story. Maybe and see yourself there a little bit. You're like, Oh, I really like this. Well, I think this is a good fit, you know, uh, and that's where bias creeps in. So it just happens to be that that kind of been on a similar journey. You know, they also went maybe abroad for their bachelor's degree. Like, okay, so they must have, you know, experienced similar, uh, some similar experience to me, I think that made them more resilient. You kind of starts rationalizing things that you're not, you shouldn't be rationalizing. All people are different. Uh, and the specific role that, you know, you're hiring for. Has, you know, an objective list of criteria. You should be just looking at those really, not the story. But not the narrative. So, yeah, I don't have kind of the full answer, uh, in terms of how to make it more objective, but I definitely kind of, my gut feel tells me, At the moment, the amount of bias in the process is probably shocking. Uh, I, I am not personally aware. So when you say, like, yes, when you read the names, etc., you can infer gender, ethnicity. That's right, but, um, I, I don't feel I have enough self awareness personally to notice that, okay, this name evoked this kind of bias in me, right? I, I actually don't know that. I'm just reading it, and then subconsciously maybe I made some conclusions, uh, but I can't, I'm not aware of them actually. Uh, so I feel the way to make it an objective process is, uh, I think only to show information that is highly relevant to kind of success criteria for the role, uh, and I think it should be much simpler, perhaps. You actually, you don't need that much information about the person. You need to know. Kind of are they capable of, of, of doing the task, uh, and kind of fulfilling the role? Are they motivated, uh, o of, of doing that role and be kind of, I guess logistically when can they start, uh, how, how soon can they start and is there anything over the next kind of two years in the plan that can sort of, uh. That should be a consideration, whether we should hire him or not. And kind of that's it, right? Uh, I think everything else, uh, sort of should be, yeah, should not be part of the selection criteria. And then there is this cultural fit, of course. And that's where, you know, I, I, You could be kind of controversial and say you shouldn't do a cultural fit. Like if the person is fit for the role, but you know, they should accept it. But then in reality they will be co located with other, other human beings and they need to be able to form some sort of a connection with them. So yeah, so, so I guess you do, you do need to have some conversation and that's where all that information will be potentially revealed. Uh, but at least from the technical perspective, Uh, I think it can be quite a, uh, objective, uh, sort of, uh, super, uh, precise even process. And then there is maybe the cultural, uh, the EQ, kind of the human part of it, which maybe someone else takes care of, even though, uh, there isn't. Trained professional that does these interviews, that knows how to be a bit more objective and less biased, et cetera. Uh, but, but what I found, uh, about kind of the personality and the human element of, of hiring is it, it is important to diversify the interviewers. So if the interviews are all male. You would typically, like, you would notice a difference as soon as you introduce a female. Uh, kind of the, the, the panel discussions become very different, uh, towards the end. And then women would pick up on things, uh, in, in candidates that, you know, men typically wouldn't. Uh, and, uh, I remember some women saying, I remember how, you know, this person made me feel like, you know, I was inferior or something like that. And, you know, something I, I, I, it felt like he was talking down to me for, for example, right? And that's not the kind of traits you want in, in, in a person in, in your office that is talking down to a woman. Um, but then when you interview them as in a man to man, you, you, you wouldn't actually pick that up. So, yeah, I mean, it's a complex thing. I think there is the objective part, which is, You know, the capability, the motivation, uh, which I think can be, uh, standardized and automated. And then there is the human part, uh, which is highly subjective, uh, and I, I'm not an expert to, to tell you how that part should be done. I, I'm not sure that can be automated, uh, but I think that should be done by people that are properly trained, uh, to, to, to run these interviews.

TIM: That's an interesting thought I'd never had before. You almost might have an expert soft skills or cultural interviewer who's agnostic to the role or whatever. It's just their job is to evaluate this one thing using the most unbiased method they can as a human who's still ulTIMately biased. Yeah, that's interesting because Yeah, I feel like A lot of the TIMe we're almost winging it Like we're making it up as we go along in hiring like you mentioned even for the for the startups getting that kind of vibe that they don't quite know how to hire yet as a company And like i've never personally received interviewer training in my life I've just learned through a lot of trial and error emphasis on error Okay that I figured it out over 10 years how to do it less crap than I did it initially You Um, but surely there's just a, a generally, a general set of best practices of how to do it in the right way. Um, and the, the difference between having an excellent interviewer and a person who's never interviewed before in terms of the end outcome must be staggering. So, yeah, imagine we might end up having professional interviewers. That's an interesting concept. Um, I feel like even though that, that step is subjective and yeah, maybe it's less subjective with an expert. I feel like you can still make it a bit more objective by at least adding some numbers. So it might be someone's opinion over, you know, this person's communication skills or their demonstration of this particular company value. But if you just get people to measure it and say, well, give them a scale, uh, give them a mark on a scale of one to five, at least then you've jilted down to a number as opposed to, I didn't like them, or I didn't feel like they're a good fit, which is where I see a lot of cultural fit interviews go quite poorly is it just ends up as this. general vibe feeling at the end. Whereas if you force people into numbers, it could help a lot. I think

DAN: Yeah, I agree that standardizing the questions helps. I think definitely there should be a standardized structure of that, uh, kind of softer side interview. Uh, but then numbers, I, my suspicion is that, uh, my hypothesis is that, you know, if you don't like someone, uh, like numbers won't, won't help you if there is a bias, uh, you know, awesome sort of. prior experience you had in your life that you associated that person with and you're punishing that person just because, you know, he or she evoked that memory in you, for instance, you you will find a way to rationalize it or your subconsciousness will kind of find a way to do that. So I, I'm not sure kind of numbers themselves will solve it unless you have A very, uh, kind of, very descriptive, uh, criteria for each number, like five means, you know, one, two, three, four means, this, this, and that. Maybe that can help, but, uh, I mean, it's probably a whole science, uh, yeah, which is, uh, I'm sure full of, uh, also unknowns and biases, et cetera. I do feel, the more I think about it, I do feel people should be trained for this, and I agree there should be a structure, but also there should be a trained professional, uh, that is aware of, uh, Um, and, uh, you know, biases and kind of knows, as you mentioned, right, like if you're hungry or not hungry during an interview, you're kind of, your mood is going to, so your scores are going to vary a lot. So, someone aware of all these things, uh, and also maybe there should, you can't have, you can't ever have one person, uh, who kind of has the final say. You should have sort of a few people, uh, that kind of represent different perspectives.

TIM: Well, one thing I'm struck by DAN is all the data leaders, like analytics and data science experts I've spoken to in five years, I reckon only 10 percent of them. Would be of your mindset, which is no, no, no, we need to do this objectively. A lot of this stuff is measurable. Let's set up a scorecard. Let's try to do it in a structured way. 90 percent of them would just go, no, I trust my gut. I trust my intuition. And they basically do the winget approach, which I find bizarre in that these people are leading analytics functions in product and sales and marketing operations. They are getting up there and doing the in team meetings. You know, vouching for the power of data driven decisions on one hand, and then they go to hire someone and they just go to the pub and have a chat with them or a similar kind of vibe check. Do you have any, any hypothesis as to how those two things coexist in their minds?

DAN: Yeah, that's an interesting question. Well, I guess my mind went to kind of these stories about Steve Jobs, how, how he would interview people, uh, and he would kind of almost stage like a theatre play, uh, pretend like he's not paying attention, uh, and he would kind of do unexpected things in an interview, uh, and I've, uh, Yeah, and kind of carry them out in a strange format, and then somehow that gives him enough signals to conclude whether this person can operate under pressure or not. And so he had like, I think, one specific, uh, sort of, uh, well, maybe two things that he was looking for. Can they, can they kind of operate under pressure? And I think it was something else. So that, that could be, uh, maybe, um, Yeah, it's difficult to kind of, you know, to pinpoint something, one or two things that will explain all the 90 percent of people. I guess all of them are different. They have their own ways. Um, but in my mind, what I personally look for, but kind of based on my gut feel, is, uh, curiosity. So I think when I see, uh, in the candidates that there is that genuine curiosity for the subject, that's what And they're actually, and they have, and there is track record that they actually have learned a lot about the subject, uh, in the past 6 or 12 months. Uh, and, and, and, and they're humble, you know, and they can, they're, they're a continuous learner. That kind of could be kind of a decisive factor for me on a kind of gut feel basis. Maybe that's not something you can, you know, Objectively measure, actually. It is something you kind of feel, uh, perhaps, uh, during the conversation. So, my guess would be that these people have, you know, these specific traits, personality traits, uh, they're looking for. And they just trust, and the best way to do that is just rely on your gut feel. Because actually, as humans, we're quite good at reading people in terms of, you know, are they genuine? Are they curious? Are they competitive? Right? So, like, there's a personality trait. So maybe they're looking for specific traits, and the best way to identify them is By trusting your gut and just kind of trusting your instincts. Uh, so that would be my hypothesis is, yeah, they're hiring based on personality, not based on, uh, sort of capability or, uh, anything else.

TIM: Yeah. I think you're right. And they're probably pattern matching to what they're expecting. Um, but I, I think the challenge with it, well, there's many challenges with their approach. One of which is, again, it's the unconscious versus conscious thing. If they just started the hiring process by listing out all the things they really wanted from a candidate, they'd probably uncover things that they were kind of secretly screening for in that pub test interview. But if they got them down on paper, then at least you could have a conversation about, okay, you're looking for someone who's, you know, resilient and trustworthy and works hard and whatever. And they'll probably discover, hang on, one or two of these criteria are bullshit. Um, it turns out you're really just looking for someone who's exactly like yourself, for example. And so bringing these forward in a conversation could give a level of self reflection that would then make you think, Oh, you know, right. Actually, I get it now. Uh, I'm, I'm just, yeah, picking myself again. Uh, so I feel like even if you're going to go for the gut feel approach, at least do it in a conscious way, I guess, would be my

DAN: Yeah, that's an interesting point. I guess, yeah, I, I suspect that a lot of people are looking for, uh, themselves, uh, Kind of maybe five, five years ago, 10 years ago in the candidates, that must be such a ego pleasing thing to do.

TIM: Wonderful. Well, DAN, thank you so much for the conversation today. It's been really, uh, insightful, wide ranging, and, uh, yeah, thanks so much for sharing your thoughts with our listeners.

DAN: Thank you, TIM. And thanks for everything you're doing with Aluba. And I hope, you know, I'm really having my fingers crossed that you'll achieve the vision of making the hiring for data roles objective and pain free. Yeah.