Episode 98

Meenal Iyer on AI, Storytelling, Future of Hiring & Bridging Technical Brilliance with Business Impact

TIM: We are live on the Objective Hiring Show with Meenal. Meenal, welcome to the show. Thank you so much for joining us.

MEENAL: Thank you so much, Tim. I'm super excited to be here.

TIM: I'm pumped to have you here, and it would be great if we could start by just hearing a little bit about yourself, just so our audience can contextualize our conversation today.

MEENAL: Absolutely. Hi everyone. I'm Meenal. I head the data team at SurveyMonkey. So that means everything from data engineering all the way through data science, AI, and ML. I've been in the data and analytics space for over 20 years now with X with varied industry experience. So giving me a little bit of an edge when it comes to solving problems under different business domains and different industries. So again, I'm coming here to share a little bit of my experience. And then, of course, learning about what's going on in the market as well. So thank you again so much. Tim.

TIM: It's my pleasure, and I'd love to start off then just by asking you your view on the current LLM hype, given you've got this kind of longitudinal view and you've been in the industry for quite some time. You've probably seen different hype cycles. The big data, this and that. Is this the real deal? Is this as much of a transformative turning point as everyone seems to think it is?

MEENAL: So absolutely, yes, I believe it is a transformative turning point, and very much so, right? I believe this is the case because one is that you can see that the evolution is not now happening. across months. It's now happening across hours and days. And the things are just becoming more and more different every single day. So it is obviously the responsibility of leaders and us as teams to essentially start looking and seeing how we can slowly start integrating this one within our hiring practices and second within our teams themselves so that they can evolve to what is going to be the role of the future itself. I see that this is something good. And I see it. This is something that is going to make us productive. And it's going to just make us work on much higher-level things rather than focusing on the low-level things themselves. This is truly a transformative time, and we all should take full advantage of it.

TIM: I'd love to hear a bit more about how you've used it in your personal life. I'll give you a quick anecdote about myself. So just on the weekend. I was using Chachapiti to help me with my Russian. So I was getting it to give me, like, a little Russian lesson as a tutor. And then I also got it to look at the soil in my plants and tell me, like, what's wrong with the soil? How can I fix this? And it seems to do a pretty good job at both of those things. And it was quite amazing and effectively free. How are you using AI in your own life?

MEENAL: So in mine, I use it a lot for one, which is my trip planning. So, for example, I wanted to do a quick three-day vacation about two to three hours from here. And I was, I wanted to do look at some of the waterfalls that were there. And as I was digging, I realized that there are 10 waterfalls within a certain radius. And so I told it I use Chat LLM from Abacus, actually, which gives me access to a lot more LLMs than just Chat GPT, but I asked it to tell me how best I should organize my three days. And I was taking my Tesla along with me. So I said, okay, tell me, so that I can even charge my Tesla on the way if required. And it just gave me such an amazing itinerary. And I followed that to the letter. I, of course, came back and gave that chat GPT even feedback to say that, hey, I used it, and this is what I found was good. And these are the areas that could get fixed. And I thought that was a good way. The second way I use it is for my weekly planning of what to cook. It gives me a sense or an idea as to what I should be cooking for the appropriate level of macros, how much protein and everything I should be having in my diet, including the recipes themselves. So it just makes your grocery shopping that much easier and that much more to do. The third thing I use it for. As we all do, we set new goals every year. And so I tell it that, okay, these are the goals I'm setting. Can you give me essentially ideas as to what I should keep as my milestone so that it becomes a smart goal? And so it gives me a good structure that I can follow. In some cases, it's really good. In some cases, I need to tweak stuff a bit. And use it. But these are the ways in which I have actually used it. I also use it as a thought partner when it comes to thinking about it. How can I incorporate AI into the data strategy itself as to how I should be thinking about how my strategy should evolve and how we should slowly bring AI. into what we do? And it's been an interesting experience. So I think, as I said, we just need to use this in the right and effective way. And there are a lot of benefits that can come out of it.

TIM: Yes, the right and effective way. And I feel like at least my observation in the hiring space is that probably candidates have picked up AI very quickly and have started to use it to write their CV and augment their CV. In some cases, try to take tests, maybe even as an interview assistant, to put it nicely. How have you seen AI used in the hiring process? Have you started to dabble with it on your side at all?

MEENAL: So I haven't tried my resume, actually, to see how ChatGPT would do it. But one of the things that it has actually helped me with is to basically summarize my experience. If I have to talk to someone in terms of what my whole experience translates into and what is my next. Step, or what does my next step look like based on my resume? I got that done out of ChatGPT. But in terms of just resumes that are coming through for rules that are open within my organization, I have seen people starting to present their resumes more defined to the job description. So I believe what they are doing is that they've sent the job description and have ChatGPT, kind of. put their resume around that. So one is that in some cases it provides a little more clarity to the role than what it used to, because in some cases what it would do is their resumes used to have exactly what they used to do. As just descriptions, but here it takes some of those wordings and translates that into impact. And then you can start asking questions about the impact. Now, the flip side of it is also the fact that, when folks write their own resumes, you could very easily make it out just based on their writing. As to whether there were things that you could notice within their writing that indicated that they may not be that good from a written communication standpoint, we have to see, speak with them, and see if orally they are better at it. But with ChatGPT, it becomes harder to describe and understand as to whether there is going to be a challenge from a written standpoint, because typically interviewing is you speaking to the individual. The written piece of it, not so much. It is only later, once they get onto the job, that you realize, okay, there is going to be an issue with writing, and you're probably going to need to work with the individual on the writing itself.

TIM: Yeah, that's such an interesting point. If suddenly all these CVs are written or at least augmented by the same LLM, then I imagine they'll start to sound a bit the same as well. So it's not only just that you don't have the sense of the candidate's written skills of that CV step, but also do they just, do the CVs tend to look more like each other, and therefore is it more difficult to pick which ones you would like to shortlist? Do

MEENAL: Yeah. It, I would say. Now, unless they actually had the full resume truly written by Chad GPT versus uploading their resume and then getting it written, I still see that the resumes look at least different enough that we are still okay. But you can see certain words being reused. a lot, like pioneered, led all of that. And then when you question them, it turns out that they were not leading the team. They were actually just part of the team. There are a lot of those nuances that you can, you have to quickly catch up on once you actually speak to the candidate itself. But I have, so far I haven't seen where things are looking too similar and two resumes look too much the same that I'm like, Oh my God, this is like a total chat JPD one. So far I haven't come across that.

TIM: Do you have any sense that the truthfulness of the CV has dropped once candidates have started using ChatGPT to improve it? Are we getting further away from reality, do you think?

MEENAL: I think so. I think so. Because, as soon as something or someone starts rewriting your resume. Then you already know that, as I mentioned, like the written, your written ability essentially is masked. So now I no longer have access to what your true written ability is, like everything starts to look a little more perfect and more organized. So it becomes harder to make that judgment call when you just look at the resume itself. And then secondly is the fact that. Say someone truly was just only a participant in a specific project or something like that. The way ChatGPT represents it for a senior role. Or for a specific job description, it could also be twisting the truth a little bit. And you will realize again that this is something you will realize as the interview progresses, and you will say that, okay, this is not a candidate that we should have picked up. And we would have probably missed another really good candidate who we didn't see or. notice having that same. So I think there are some of those things that we're going to start noticing, and we have to become intelligent to be able to glean those things out more automatically. But it's a process right now. I'm saying that we have just started that, and we are not yet at the point where we are able to distinguish really well and say that something was generated from ChatGPT versus not.

TIM: I wonder if part of the issue is just the mindset and even the terminology. So when an AI lies, we say it hallucinates, which is a euphemism for it's bullshitted. If we kept using the word "lie, I feel like we'd be a bit more honest rather than coming up with this fancy-sounding term. I wonder also because people have used these tools, and it's the tool that's written the lie or the hallucination. It's almost like you've outsourced or Cutting off yourself from actually sitting down there and writing something would be a lie. So you can almost separate it in your head. I wonder if it's just, yeah, we'll have to go through and see how we use these technologies as much as anything.

MEENAL: I believe so. And I know the funny thing is, they have these humanizer tools. Where, it humanizes it, and it looks at the way you have written and tries to copy it. The funny thing is that the humanizer is an AI too. Yes, I think there is going to be an evolution. But if it turns out that the evolution is again an AI. Then you're probably going to be in that loop again and again, and you're going to be trying to humanize it. It's funny. I was looking at this just this weekend. I was looking out just to check out these humanizing tools. And there are a lot of them. And it says that we are more human than the other. And I just took a snippet of code that I just copied off of ChatGPT, like a ChatGPT output. And I put a code of how I had written it. And I was like, okay, write this in the same way. And they all had variations, but none of those variations looked like how I would write it. It was funny. Like, I have certain quirks and nuances that I write like in the way I write, and I didn't notice it copying any of those. And again, coming to these humanizers, people are going to try to get the resumes more and more perfect to the point of where it feels really written by someone. But, again, it's going to be AI who's going to do that task. And I feel that that is not going to solve it. But yes, there is going to be an evolution. And I don't know if things are going to start. Being or looking a little more real. But this is the state we are in. Unfortunately, all job searches are happening through AI. So folks are being told, you just create one resume, and then you put it out to this whole AI engine, and it'll push it to a thousand jobs. So you can imagine what happens. It's just going to be this whole thing is going to be very interesting; it's going to be very interesting going forward. So we as leaders who are looking at these kinds of resumes, we really need to get smarter and practiced in terms of how we should truly be processing these. Because every position gets over a thousand resumes. So if they're getting over a thousand resumes, like, you want to pick up really the cream of the crop that you would want to bring in within your organization. So we need to get a little smarter in terms of understanding which ones are being worked on versus which one is the original resume itself.

TIM: And how are you currently dealing with that overwhelming volume? And then where could you see things going, like, in the next year or two? What would be a better way of dealing with that volume? Do you think?

MEENAL: So right now, we manually check the resumes; each of us takes resumes, and then we just look through them and say, Hey, good one, bad one. So that's the reason, and I don't believe, I don't remember when we had our conversation before, whether we talked about the fact it is preferable now, if it comes through a referral. You have higher chances of your resume getting seen because you just have so many candidates that apply for that same position and role. And I, right now, it's a very manual check, and you have to be like, okay, if you really are a candidate, you just go quickly check their LinkedIn. And just quickly see if they have references that are put over there, whether there are references, and provide a very common message that indicates that this individual does have the skills that you would like for someone in your team to have. Unfortunately, the technology skills piece of it, everyone has the same thing on the resume when it comes to technology. And so that section is harder, but then if you look at the skills that you need in terms of logical and analytical thinking. That also doesn't very fully show up on the resume unless someone has said, Hey, this is the impact that I have essentially created. So again, doing a manual check right now, I would think that future systems, like how we could program it to see if we have people put in resumes. Based on a certain problem that we have given or provided to them, we can then see how they respond and then figure a way in which we could actually know that this is not done by a chat GPT or something like that. When they actually respond to that, because otherwise it's just going to become harder to go through these resumes, because each resume is going to pass through ATS. So it's not even like everyone will have those keywords in, so it'll all just pass through, and you are going to have to look at each of these manually. I would think we just need to get a little, a little more creative in how we put our job descriptions out. I've already seen people on LinkedIn doing it a little elegantly. And these are for the higher-level roles, executive roles, of course, where they describe the role in very interesting ways. And people have to respond to that. So it's almost like a question and answer that they have, but these questions are not questions that Chad TPT is very readily going to be able to answer based on your experience. So I'm thinking something along those lines is where we all have to be very creative in our job descriptions, then have it exactly the way it has been done for, what, hundreds of years or however many years. So that's where I see, or that's how I think it will evolve, but that's yet to be seen, but right now it is manual.

TIM: Yeah, it's such a tricky scenario, and it feels like we're just in this kind of intermediate. Scenario where it's broken a little bit because AI has come about so quickly; candidates have adopted on mass, companies not quite, or definitely not at the same speed yet. And I feel like we just need to wait another 6 to 18 months, see the next evolution of the, let's say, foundational AI HR tech, like some new wave of HR tech that's come through, and hopefully solve this. But to me, I feel like one of the sticking points is still going to be. The data quality that we have access to in those early stages, because whether it's a CV written by a human, written by AI, a LinkedIn profile, or whatever, it's still such a limited data set to figure out who's worth interviewing. Is part of the unlock going to be that we need new data, like we need to have better measurements about people that are more readily available, do you think?

MEENAL: Yeah, I believe so. We will have to change the structure again of how we present our job description in a way that people have to write something very nontraditional. It cannot be the traditional way, and it will also be doubtful anyone who is truly not interested in the role. So we find a lot of people who are not truly in the role. They have just applied it because right now the market is so bad that people are just applying for positions. So a data engineer is applying for an analyst role or a data scientist role, but there's nothing in their resume that reflects it. Your chat GPT essentially writes up your resume in a way to indicate that you show data science as a role. And it's just very weird, but if we start changing the way, or if we make it very creative in terms of them executing the problem, I'm sure it'll weed out some of those folks who truly don't know. Or have an understanding of what needs to get done. And we should be able to get the number of candidates to a much more decent amount. But yes, we will have to figure out what's the best way to do it. So that we are not pushing a lot of people away. I know there is a lot of frustration also around where they're given offers and then offers are rescinded, or all of those things happen as well, and so candidates just are having a lot of fatigue and burnout around that. We have to ensure that we take that into account as well, but yes, the process needs to change. I think there has been no evolution in this space in a very long time. Just as we are having a conversation right now, I'm thinking this is, like, really ripe for disruption. And really, right for a change in the way we do job descriptions and the way we hire, there is a lot that needs to get done in this space.

TIM: Yeah, 100%. And I feel like, again, in the interim, things might not get better; they might get a little bit worse in some ways, like you mentioned. For a candidate, the best way to get a foot in the door is through a referral. Which makes sense. If I were a candidate now, I'd be trying to, like, backchannel that, use my network, and try to get my foot in the door that way, but that's a little bit unfair. You could say that means for the thousand candidates who have applied. A lot of them don't have the same chances. The person has got the referral. So I feel like maybe that's going to cause some fairness issues in the short run because It's going to be

MEENAL: Absolutely.

TIM: More likely that the person who has good contacts is going to get the job then. Yeah. Versus the best candidate for the job.

MEENAL: No, I fully agree. especially folks who are especially folks who are introverts, who are not into networking, who don't spend their day on LinkedIn. Trying to make new contacts, those guys, those folks, are going to struggle. And it's just unfortunately the nature of how things are. And so yes, there is a lot of fixing that needs to happen. But there's a lot of macro impacts that are causing this as well, right? Like, you have so many people on the market. And that is also causing their companies to have to look at, okay, if I get a referral, at least I know it's someone who someone knows. And that's an easier in than me having to go and manually sift through like 1000 resumes. So it's that as well. So yes, everyone, we all have to evolve with the times and know that, okay, if this is the thing, then we just need to get better at networking, and we just need to get better at seeing how we, how RSU, can get seen itself. Now, the worst thing would be now if all the thousand people brought in referrals. Then it's going to be really bad. I don't know exactly how to solve for that. Unless the first referral we get is the superstar, and we say, okay, that's it. We hire this person. But that could be the other challenge as well. So right now I'm just happy with the state of having those minimal referrals and then looking through those and then sifting. If we don't find a good candidate, then sifting through the rest of our resumes.

TIM: Yeah. I wonder if someone will try to scale the referral process and build a product out of that. My only thought is why that wouldn't happen is if anyone refers someone that's. The value of your words carries a lot of weight. If you referred someone who turned out to be useless, that reflects very badly on you. So I feel like there's at least some limit in terms of how many referrals there can possibly be, which then might still make them a really positive and useful signal in hiring.

MEENAL: No, absolutely. I do get folks who ask me to refer them, but I make it very clear that this is just like a LinkedIn Network Connect referral, and it's not someone I can provide any basically not talk to their credibility or their commitment or their work ethic. So I indicate that very specifically so that, tomorrow, it doesn't—you're absolutely right. Because I believe that if I refer someone, I really know them well. And I can speak to their capability. But if I'm unable to, I let the candidate know, and then whoever I'm referring to, I let them know as well. Then they make the choice as to whether this person is the right person or not.

TIM: Back to the style of conversation we were talking about, like good and helpful uses of AI. One of the use cases we've mentioned here is with candidates applying en masse to roles with, let's say, Chachapiti-optimized or tweaked CVs. Is that undermining their chances in a sense? Is that maybe not the best use of AI? If so many other people are using the same model and approaching the job search in the same way? Would it almost be worth doing something like that? What do they say? I think in America, they say Zig when someone zags—that kind of philosophy. If you were searching for a job right now, would you apply through job portals and compete with a thousand other candidates using ChatGPT? Would you? Try to do something quite wildly different.

MEENAL: So my wildly different approach would be if something basically came through my network or if I was truly starting to look, then I would look through my network first and ask around. Say my previous bosses ask them, Hey, is something open or is something coming up? Soon. So that's the way I would do it. At some point, like within your roles, that resume doesn't make as much difference as someone who knows you and talks about you makes that much of a difference. It's more in the more technical roles and the lower roles where you are going to meet the resume itself to come in. But yes, there is going to get to be a point of where. This process is just going to become harder, both for the applicant as well as for the interviewer itself. I don't know if I'm going to say things are going to start looking similar in terms of resumes and applications, but things are going to look similar in how people are going to look at it. And without the lack of a person who can literally talk to or about you and say that, yes, this person did a good job, there is nothing else. There was this time where on LinkedIn, you would have people provide you references. It's, it's no longer used that much, but you would go and get references in, and then, for a while, it was good. Then it turned out that how people did it is like, Hey, I'll give you a good referral. You in turn give me a good referral as well. It's slowly turned into that. I think. At some point, this is going to turn into something similar that way, where it just becomes something that is no longer unique to you or tells what you are capable of. And we're going to lose that messaging in all of this that's happening. I don't know. We will have to start seeing as to what's the best way in which we are going to have to start to tackle this. I don't have a ready answer for it, unfortunately. And you have just got me thinking about possibilities of what we can do to make changes and/or shifts in the space. But no ready answer yet.

TIM: You don't have the magic bullet. You don't have the panacea. Are you kidding me? Come on.

MEENAL: wish, but I'm loving this conversation itself because, as I said, you're just giving me ideas that we need to disrupt the space sooner rather than later. Things are shifting, like things are changing very quickly, and we need a good solution for this. So hopefully someone who hears this comes up with a really good product and a really good tool to help solve this.

TIM: Yeah, I keep coming back. And again, I obviously don't have the answers either, but I keep coming back to just, We're trying to predict who the best candidate is from a thousand candidates. We're trying to find the best candidate for this job. That's a matching problem where we need good data. And the quality of our prediction is very heavily weighted on the quality of the data. And I just keep coming back to CV this, believe it or not, 500-year-old document. I think it was, what's his name? Da Vinci was the first ever CV he had 500 years ago. Like, we need something better than that. Like, even before AI, I feel like the problem with a CV was. It's just someone has written a document about themselves. Like, it's only one or two pages long. How much can you really know from a document that's written by someone? It's not really validated. I could claim to be a rocket scientist, but I can say I'm not; you just have to take it at face value. And so we just need something closer to the truth. I wonder if a new product might emerge, which is if you think about the start of the hiring process for any role. Most interviews would correlate quite closely, like you'd be wanting to understand a bit of the person's history and this particular skill and evaluate their communication skills and what have you. Maybe we need some kind of intermediate product that just, as a candidate, you go and do answer all these questions that any company would be interested in, and then we use that. As the matching data rather than a CV, because it's like a little bit closer to the validation of reality. I Yeah, it's almost like a lead code or an SQL checker or something along those lines, but we need to do it, for example, like in data; it's a lot about logic, the ability to create impact, and the way of thinking creatively and out of the box. If we can test those capabilities as part of these products or platforms that we can bring in, similar to how Lead Code does, but people have started gaming Lead Code as well. Okay. We need this system to be very, what to say, it should evolve. So with every person, it just needs to ask a very different question. Then, yes, probably, we can start bringing in candidates based on that. So that's not to say we won't test them for their technical capabilities if their role requires it. But you get a sense of even those other soft capabilities that you cannot see or look at in a resume itself. And yes, I agree. That's something that needs to get created. And hopefully that will make the process a little easier right now, because if you think about it, all of the talent platforms that collect the data, the way they weed out resumes, is literally on keywords. You have the right keyword; your resume is in. You don't have the right keyword; your resume is out. If the chat GPT knows that's the case, it will actually game your resume to have all the appropriate keywords. So your resume does get in, but I still don't understand or know your logical and analytical capabilities. And again, I'm talking very specifically about data roles. So I don't know those capabilities, and I'm going to be again stuck with having to. Glean from the resumes: Is there anything that I can pick up from there, short of not picking up the phone and talking to the candidate and understanding that myself? So yeah, that kind of product, at least I don't believe it exists so far. Speaking of that, we were talking before about maybe the truthfulness of the CV may be eroding a little bit. Have you noticed any drop in the conversion rate? That is, of people selected based on their CV to that interview performance. Is that conversion rate getting lower, which I think would be almost an indicator of the CV maybe not representing reality as much?

MEENAL: Yeah, it has. I don't know; like, I have never tracked that very specifically as a metric. But unfortunately, like the challenge continues to be the same. Out of, say, a hundred or 200 resumes, we get only three or four of them to actually make the onsite or the panel. And then, from that, you will get your, like, your best candidate if you're fortunate. And then, of course, a runner-up, just in case the best one does not decide to take the offer itself. That continues to be the case right now. The only difference is that initially you would get 200 or 300 candidates per role. Now you get over a thousand, and you still have the ability only to look at the first 200 or 300, because if you're checking stuff manually, then that's the bandwidth that you currently are running with. And the conversion is about the same, which is, you get three to four people from that list on site. And then, you choose your best candidate, and you run it up from there. One of the things that does happen more frequently is where people come all the way to the offer stage. And they are given the offer that they asked for, and then they reject the offer. That is happening a lot more now. So it's not only like an employer market, but it's also an employee market. And. That is something that has become more frequent. We have had some instances of that happen, which I don't believe, like I, at least I hadn't experienced in all my hiring that I had done previously of this kind of thing happening. Specifically, where you're given the offer exactly as you want it, and then, after the offer is provided, you reject it and pick something else. That has become more common in such places. So that is one of the shifts we have seen. But I think from a conversion rate standpoint. It is pretty much the same. It's just that our denominator has increased significantly, though.

TIM: Yeah, that's such an interesting phenomenon, isn't it? Because you'd expect if it's this seemingly employer-driven market where there's a high volume of applicants, then any candidate who did get an offer, you assume, would be snapping your hand off if you'd given them the offer at the rate they asked. But they're not. What does that mean? That's strange. Does that mean there's just generally less trust in the market, maybe? So candidates are like, I don't care; I've already been screwed over by all these companies where my CV's been rejected, so I'm just going to think of myself. Is it that kind of mentality, or is it just the fact that ultimately the best candidates somehow find their way to the end of all the processes? And so then they have lots of offers in hand. Do you have any idea what might be happening?

MEENAL: I think it's the second one where they have multiple offers. So they are really good at what they do. And, if you have the ability to really snap them up, then you are obviously going to have to pay them way above market. Some companies are able to; others are not. And so you basically lose those candidates. The unfortunate thing is that you have dragged them on through to the end of the process. And now you have to go back again. And your runner-up may not be someone you wanted, then you have to maybe compromise with a lesser candidate. So there are instances of those happening, but I believe that the employee also has multiple offers. Nowadays, for the right candidate, people are paying equally well. The candidate also knows and realizes that, hey, I'm awesome. And I'm going to get compensated for that awesomeness.

TIM: What at the moment then is differentiating those awesome candidates? Like, what type of candidate for a data role is going to end up with multiple offers versus maybe not even a first interview? What's the real value add? Do you think

MEENAL: So the real value add again comes from the way they think about answering the question. So when it comes to our, say, for example, I'm hiring for a data scientist or an analyst role, we give them scenarios. And we tell them that, okay, this is a problem, and this is what we need to do. Let's walk through it logically as to how you would do it. And we give them a couple of days, and they have to come back and do a presentation. And this presentation is to a much broader audience. And questions are asked at the end of it. To essentially gauge whether the thought process around it was good and sound from a business standpoint, whether it helped answer business questions in the way that it was supposed to, and whether it was able to dictate or show impact as it was supposed to. So we get folks from all of these different areas and have them sit through it. And I have noticed for candidates who are really good. This is the place where they really shine. They really shine. They show that they have the ability to speak both languages, like the bad language of the business, as well as speak the technical language. And that's where they truly shine because they are able to cross that bridge. For the ones who don't get there, they are really good technically; the second bridge is that they are unable to cross. I see that those candidates, once they realize and know that ability, then, they pretty much are confident that, hey, what. We have a good chance of getting through your once we get to the interview process itself or once we get to a point where we start talking to the right people. And that I believe is the right person that you want for your role as well. Unfortunately, their resumes don't tell you that story. Then they have to be lucky that they get essentially picked up because you have a lot of ChatGPT-generated resumes that also say, Hey, impact created, I increased revenue by this much, or I did this much, or I led a team, and their resume will essentially show up the same as this individual's resume, but then The unique part will be that once they come to the second stage, that's when you see these guys shine out, and they become like your best candidate.

TIM: It sounds like then, if we think slightly bigger picture about, let's say, the pool of data talent. A bigger gap at the moment for them would be maybe the softer skills, the business skills, and the networking skills than the opposite. Because it's maybe limiting their job search a little bit because they're applying en masse rather than being able to leverage a network. It's limiting their interview performance because you're more likely to fall down from a lack of, let's say, business hat skills and communication skills than you are for the technical skills. You're saying that technical skills are maybe not commoditized, but like you're saying, you're seeing the same list of tools on every CV. So it's really the other bits that are differentiating. Therefore, would you recommend to these candidates that, on average, they're focusing more on those other softer skills rather than doing, like, another Udemy course in another tool?

MEENAL: Exactly. So yes, understand the technology for sure, but then also take the other side of it, where you have the ability to do the storytelling on what you build out. I learned how to build my own LLM model. But if I can't translate that to what impact it could create for my business, then what's the benefit? And this is the storytelling that they have to start becoming comfortable with. So I do encourage folks I talk to; I encourage them, one is, start building out. You have a passion area; you have a passion project; basically, start building out on that. And once you do that, I said, the first thing that you have to do is when you go to a company. Talk about how you brought the impact you believe this will create. Now, in some cases, people want to benefit the broader community, or it's a social cause, and I said tied to that social cause and talk about how you believe that this, what you are building out, is going to change or evolve that social cause. And every project that you do has an ability to be tied to a story that you can actually tell. And if you have the way of telling that story appropriately, then you are a really good candidate, and the chance of you getting hired is much, much higher. I've hired mechanical engineers and chemical engineers in data projects who have no data experience prior to them joining. And the reason I did it is just because I saw this side of their skills shine so much more. And it creates a lot of difference. And then you see those individuals, even once they get on the job, they work to make sure, of course, they work to bring up their technical skills up to par, but they are your shining stars. I believe that yes, technically, I'm not saying technical is not important, but there is this other side of the skills that you have to start slowly making sure that you get to as well.

TIM: And to connect the dots fully then, is that part of the importance of this skill that once they're actually in the role? The value they can deliver to the business is very positively correlated to how influential they are, how well they can tell a story, as opposed to being locked in the corner, beavering away, building their model, and nobody really cares because they can't communicate the value of it.

MEENAL: Exactly. Exactly. And that's the challenge, right? Organizations typically say, Hey, data is an I.T. function. And why do they consider it an I.T. function? Because they may be good technically. And, you tell them to do a specific task; they do exactly that and give it back to you. In no way are they showcasing that, hey, I could have done this in addition to that. And this could have added more value to what you are already working on. The question of why I am doing this never comes up. And so data gets relegated to being an IT function. And our goal and our job is essentially to say, Hey, data being the new oil and being the biggest asset that any organization can have, you cannot relegate it to an IT function. There is so much more that can come out of it. And that's where each of these folks and these teams have to realize that's the value that they bring to it. So it's not only the leaders but the individuals on the team itself. They have to have that ability and that capability that they need to start building on. So storytelling is a big piece of anything and everything that you do. And that comes with a lot of curiosity and a lot of wisdom in terms of questions. That you need to keep asking till you get an answer and understanding of the impact that this is going to create. And yes, I think that folks should fully understand that and make it a point to work on this other side of skills as well.

TIM: To play devil's advocate a little bit, is there at some level a responsibility to carve out space for people who don't Are just never going to be the soft skills, communicative networking types, but maybe an utter genius. And if we could leverage their skills in the right way, we don't have to put them out in front of the business doing presentations as long as they can communicate to their manager. That's enough. Their manager can then do the comms for them, but almost not over-indexing the hiring process to include the software skills. If sometimes, for just the individual contributor, you get a genius with 150 IQ, they're going to deliver a lot of business value. It's just, they need some help.

MEENAL: No, absolutely. And that has happened, like, all five fingers are not the same. So not every employee is that perfect blend of business and tech for sure. So yes, those cases happen, but the realization also has to be that even with these individuals who are just so task-focused and doing like a brilliant job. So say, folks who work on the platform and folks who work on infrastructure, they still need to be able to even communicate to that manager in terms of the impact that their work has created. So I think that either way, communication and the ability to tell the story of what your work has done is also very important, irrespective of what role you are in. We all need to make an active effort. So yes, okay, don't place them in front of business because maybe they are not as comfortable talking in front of business, but then talking either to their broader team, because if they want to grow in their role or function tomorrow, if you're a principal engineer. Fine. You just talk to your boss, and you're this one and don't present to the business, but then even your manager has to fully understand the work that you have totally done, and you need to be able to communicate that. So there is a bit of that, and they can very clearly say, Hey, my area of interest is this. I'm not a businessperson, but this is what I do really well. And then you can question them on it and then understand if their communication skills are truly able again, too. Quantify the impact of the work that they deliver in the area that they deliver. Yes, absolutely we can, and we do that as well.

TIM: I wonder if this will be a good use case of AI then, because at the moment you could get ChatGPT to. Let's say a written message that's reworded your thoughts into something that sounds nice and businessy. Maybe the next evolution would be these kinds of avatars we might have of ourselves, where we send them to the meetings and they're translating what we've done into more appropriate language. Maybe that's where we'll be in a couple of years.

MEENAL: I wouldn't be surprised at the way all of this is evolving. I wouldn't be surprised at all. But let's hope that a little bit of that higher intelligence continues to stay with us. That we all have our jobs and that we all are able to do our jobs in the way that it is supposed to be done. But I see AI changing a lot of what we do for sure.

TIM: Yeah. The robots haven't quite taken over yet, so we're still in, in with the job. If you could ask our next guest one question about hiring, what would that question be?

MEENAL: So I, the one question I would ask, and I have various ways of, I have various answers for myself, but the one question I would ask is how do you know that a candidate that you are hiring will. Will be retained and will continue to stay with you in the organization, and what would you do to ensure that you answer that question to them if they ask you that? So that's one question I would ask because you get a great candidate. Now your biggest challenge is essentially how do you retain them and how do you keep them challenged? So that they can actually provide the value that you thought that they would bring for you and the team when you hired them. So that would be my question.

TIM: That's a great question. And one that we will put at a guest sometime next week. And we'll let you know what they said. Meenal, it's been a great conversation today. We've covered off a lot of grants. Be very interesting. Thank you so much for joining us and sharing your insights with our audience.

MEENAL: Nice. Thank you so much, Tim. I, it was fun, and you gave me a great product idea. So who knows? I'll make millions and give you a cut of it. So thank you again for inviting me.