Resumes are dead. Sourcing is pointless. Applicant tracking systems have failed us. At least that's the opinion of Maya Huber, cofounder and CEO of Skillset, an Israeli-based job skills test platform. Is she right? Well, Chad & Cheese have some questions ... let's leave it at that. Have a listen and make up your own mind.
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Oh Yeah. What's up everybody? It's your favorite podcast the Chad and Cheese podcast. I am Joel Cheeseman your cohost joined as always by my partner in crime Chad Sowash. We're getting like deep, deep thought with a PhD. What's welcome Maya Huber co-founder and CEO of Skillset coming at us from Israel. Maya, welcome to the podcast
Hi guys! I'm so excited to be here.
Oh, you do sound excited.
She does yeah!
So for our listeners who don't know, you give us a quick little Twitter bio about you, and then tell us a little bit about your company before we dig into the topic.
Maya (1m 1s):
So hi everyone. I am the co-founder of Skillset, that we are two women co-founders that created a Skillset to reinvent sourcing and recruiting. Basically Skillset, just to keep it short, we are new job testing that from the connect companies and job seekers mainly on proven tested skills. No CV, that's it.
Chad (1m 26s):
Joel (1m 27s):
I feel like you've practiced that before.
Chad (1m 30s):
And a double female founder. Wow! Can I get an applause for that? Come on.
Joel (1m 35s):
You can! You can get an airhorn too baby.
Chad (1m 39s):
Excellent. So, Maya, today we brought you on because you know, Joel and I have problems with assessments. There are so many gaps out there today. There's an assessment for this. There's an assessment for that. It seems like every it's just all over the place. So can you give us kind of like an idea of the problems that we're seeing today with assessments and then kind of give us a vision toward the future. We'll start to just start there.
Maya (2m 2s):
Yeah, sure. So as you probably, you know, see for ourself in all the industry recruiting became so complex in the last decade, right? There's so many platforms and solution are there fragmented the process, you know, there's each and every process for every step of the way, job interviews, ADS, job analyses, and you do background check and you do tons of interviews. And all of that, if I believe it's because the first steps still is CVR, CV sourcing, and you can apply to job based on your CVS and your stories. And I think that assessment today created to solve the bias aspect of CV. And I think this is not the solution.
Chad (2m 43s):
So what I'm hearing is CVs i.e. resumes are shit and assessments really were created because we are using the foundational data of garbage. Is that what I'm hearing?
Maya (2m 60s):
Absolutely. Do you know people, you know, try to do to deliver the real thing, but mostly, those are stories or real stories who doesn't really count. Ooh. You know, today, why does it matter why it can predict that the fact that you studied at Harvard or you're not, you know, and I hold a PhD and I'm still saying that. I think when you know, all of these stacks and solutions out there, trying to create a much more effective, you know, process it just because we are all dealing with stories along the way, and this is something we need to break.
Chad (3m 37s):
Okay. So that is assessments then. I mean, so here's the question. If assessments were created because the CV is garbage, then if we get rid of the CV, what do we do? Right. I mean, it's almost like, okay, I get rid of that. I know it's garbage, but I have no starting point now. Where's the starting point? What do I do? How do I do this? Is it soft skills? Is it hard skills? Is it testing? Is it? Where the hell do I start?
Maya (4m 6s):
So first of all, I think we need to look at what's happening now. People are talking, you know, all the industry's talking about skill based assessment, right? But when you look at those tools, most of them even, you know, all of them actually, are still focused on words. So you ask people about their skills. You provide them with, you know, a situation or a story or a test, and then ask them questions where they need to summarize their skills and to share those skills in a written way, not to show what they can really do. So for our perspective, and this is after you now we, me and my colleague, we spent 15 years of research and practice in the field of HR interviews?
Maya (4m 50s):
And we think that the only thing that counts is your proven results, your proven skills and how you will perform your job. So it's simple as that, no?
Joel (5m 2s):
Yeah. It's real simple Maya, but let's dig into this a little bit. So the CV is irrelevant. So is the job description irrelevant because people need to find these jobs and then they test for them. Is that kind of the idea and the idea of resumes and maybe sourcing and, you know, a $26 billion business like LinkedIn is irrelevant because it's basically just an online resume. Like help me get my head around an infrastructure that's existed for over hundreds of years is now obsolete. Help me understand that.
Maya (5m 40s):
I'm brave but I don't want to fight everyone. But I will tell you my perspective on that. I think` the fact that that CV is the only, and the first step is the thing that needs to be solved because we need to provide people with different ways to put their skills up front, sometimes CVs, and for specific position, maybe I believe that the more, you know, much high, maybe C-level jobs, maybe you need to take more of that into to consideration. I'm not sure, but when you look at, you know, the common people, you and people like me, if you will look at my resume three years ago, you will never hire me to be head of product.
Maya (6m 27s):
Never because you see an HR persona and you will say, wow, what why are CVs are here? And you probably just, you know, throw it away.
Joel (6m 35s):
I got the ear anti resume. I'm just trying to understand, okay, you're pro job description. You're pro get a job out there and promote it. What you're saying is that the system needs to be, if I'm Joe Schmoe and I see this job, and I think I'm ready for it, instead of submitting a resume, I take a test. And then that test tells you whether or not I can do the job. Is that what you're basically saying?
Maya (7m 1s):
Exactly what we're doing is that. Is we provide candidates with, you know, job experiences, smart simulation tests that provide people with opportunity to experience the job, the main core task of the job before applying for it. So those are pretty straightforward tests that allow you to get better perspective of what you're about to do and what's required. And if you're able to do to it or not.
Chad (7m 26s):
Well, here's the hard part though, in a market like we're in right now to be able to take a test, to prove that I know what I know, it's going to take 20, 30 minutes at least. I mean, we're talking about entry-level types of positions, even in customer service, let's say most people won't take that time today because companies are in such dire need that they'll pick anybody up off the streets. So how do you actually build a knowledge base foundational enough to replace the CV?
Maya (8m 4s):
So, first of all, I'm not sure that's right for all people out there because they're, as you know, they're underrepresented population, an untapped pool of job seekers out there who are trying to get into the workforce and do not find any success doing that. And those people are talented and qualified, and should be part of the workforce. And I think this is something all of us need to take into consideration, but not only that, what we do, our assessments are pretty short, but we look at, for us, this is a much broader perspective than just sourcing. We help companies better understand what is the best, you know, talent or what are the characteristics of the people they need to hire.
Maya (8m 49s):
So what machine learning does is collect the data about candidates performance, and then follow those results in team hiring and their life, inside organization. And then predict what is the specific type of characteristic in terms of skills of the candidate that should work. And we are talking about the companies that hire all the time, mass hiring scale. So they need this data to better understand what are the best qualification for their, you know, their job openings, because they need those people all the time.
Joel (9m 23s):
In one of the videos I saw Maya, you were pretty negative on ATSs.
Chad (9m 29s):
Isn't everybody? I mean really.
Joel (9m 31s):
I want to rephrase Chad's question a little bit in, in my own way in saying like we recently understood or reported that 90 plus percent of people that click the apply button, don't finish the process. They bail. So if they're not finishing the ATS, you know, job application process, what are you guys doing to make it so they don't ghost and, and eject on the testing. Like what's your completion rate? Are people actually taking these tests? Do they enjoy them? Are they short? Talk about your solution to the anti ATS, I guess.
Maya (10m 3s):
Yeah, sure. For us, this is, you know, for candidates using Skillset, first of all, it's fun. And because those are, you know, a simulation, you can do on your mobile, on your desktop. Those are pretty engaging tests or experiences. I think the unique experience people candidates receive from doing our simulation is that those simulation are really upfront. Those are not situation. This is not a questionnaire that is tiring. You're actually doing the job. So you are a customer care representative. You performing calls, you answer clients, you do whatever a customer care representative does or summarize call. And so the feedback we get is the experience of our simulation is respectful.
Maya (10m 49s):
People feel that for the first time, they can put their real skills up front and really expand the job. And, you know, sometimes we get responsible and candidates and say, you know what? I've just realized this is not for me. And we think this is a great success story because this specific candidate, even probably continuing the interview, and only on the timing, we'll get to the job, you will understand this job is not for him.
Joel (11m 15s):
So people dropping is actually a good thing in your system because they may realize like, oh, I can't do this job I'm out of here. I can't complete this test.
Maya (11m 23s):
Exactly what we were doing in those cases, actually, in all cases, we met them with our position that are suitable for them based on their results. So let's say your back office job was amazing. I will offer you to be a claims representative, you know, in insurance company instead of being the customer care representative. So the overall process is that fun. The time to hire is 50% faster so they get a faster response. They get a job opening and proposal by the end of the simulation. And this is what we get from the candidate, the great experience, respectful, transparent, and focus on what really matters.
Chad (12m 6s):
So quick question with regard to, let's just say a sales position, because for the most part, you know, I can't prove that I can sell in a test. It's a little bit harder than customer service. Usually you take a look at somebody's background and if they've hit goal and how long they've hit goal and how long they, what, what kind of experience they have, et cetera, et cetera. How can you do that through testing in a very subjective type of position like sales.
Maya (12m 37s):
So first of all, on the simulation, we do perform calls, okay, you see, this is the perspective, this is exactly what the job requires so this is what we do. So they perform calls. We collect their tone of voice and the content, what they say. And, you know, we provided with scenarios that some of them are customers are interested, some are not, and they need to, you know, to offer them to negotiate terms and to offer the unique proposal that the specific candidates and students see. So we tested that actually. And also we collect their ability during the simulation, during the process to improve their results and provide feedback inside the simulation itself about how they perform.
Maya (13m 20s):
So we do have the ability to collect the data. And I want to say, we are not here. And this is, you know, we are going back to our conversation about assessment. We are not a classic assessment tool. We are here to create a new job matching starter that enable and, or maybe straighten the line for all job seekers to be judged equally by their skills when applying for a job. You know, and in terms of sales, you will need to interview our candidates and make sure that they, you know, they meet with your needs, but you will know that at the basic level, they can do that and they can do a do good. And you can hear them doing that.
Joel (14m 2s):
Are all of your tests standardized? Are they customized? Like this feels like an industry that could be commoditized fairly easily. Am I, the testing itself could be commoditized, maybe analyzing it is a special skill. Like talk about how customized these questions are. Who comes up with the questions? That seems like a really a big challenge.
Chad (14m 26s):
Seems like more tasks than questions, I think.
Maya (14m 28s):
Yeah. First of all, yeah, those are tasks, different tasks, different scenarios on each and every position. We are. I'm not sure I mentioned I all the PhD, my expertise is the future of work job analysis. We are backed by professor Joseph Fuller.
Chad (14m 47s):
Not only, not only is she a doctor, that's what, she's the doctor of.
Maya (14m 53s):
Thank you for that. And we are backed by professor Joseph Fuller, he is the professor leads the future of work program at Harvard Business School. So our data and our database comes from the American labor market, from deep job analysis. We started with the data we have from, you know, from our researchers and the data comes from the American labor market database. And we match this data with the market. We know talking to companies, talking to HR influencers, make sure this, you know, the data is accurate, and is providing good reflection of the market.
Maya (15m 33s):
So this is where we start. The simulation are not adjustable.
Joel (15m 37s):
Those are standardized.
Maya (15m 39s):
Those are standardized. The way we create them and we do it with, we have our unique simulation creator that enabled us to create those pretty fast and to stand with the changes in the markets. So we do create changes and I can mention a few, but the simulation are standardized when you're uploading a platform, you will see, you know, a customer care representative, a sales, whatever. But if let's say there's a unique need for a specific, you know, customer care and insurance will be a bit different.
Chad (16m 14s):
Real quick though, when we're talking about being able to do simulations, and I want to hire them into my organization. Let's say, for instance, my sales and my customer service organization. I have a tech stack that's going to be different possibly than the organization across the street. So can you, I mean, should systems like this be able to pivot and utilize different systems like Salesforce or HubSpot or things like that. To be able to see if that individual can actually perform the duties inside those systems that our companies use every single day.
Maya (16m 51s):
So we do not this specific, you know, specific platform, but she, we do simulate those types of platforms, of the CRM platform commonly being used. So this is the same features, but we do not use Salesforce, you know, as a specific tool or Excel or whatever. But we test their ability of people to be a tech savvy and to learn fast new CRM and actually, this is one of the things of working with us there. It's all for everyone, you know, experience a simulation, our CRMs is new. So we test their ability to adjust quick enough have what we acquired them inside the simulations.
Chad (17m 32s):
Okay. So you have situations that you set up scenarios that you set up, which are really predicated on testing, troubleshooting, and problem solving. And that's what it sounds like we, because this might be a different system than what I'm used to using, but I still have a scenario in which I know what I should do. It might not be the system that I'm used to, but I can troubleshoot and problem solve through it. And that in itself, Is that like a big piece of what you're actually looking at through the assessment process?
Maya (18m 5s):
Not only that also, you know, the navigation inside and your ability to learn fast and to, you know, and manage a big data and, you know, learn fast new, we are, you know, we are sharing, this is stories about the new product you need to sell. So we, we test your ability to learn fast, but I think what we need to focus on our system focusing on the core tasks. So it will be your ability to work with a CRM, to answer clients to is your service orientation, your, the commutation skills and your ability to solve problems. Okay. So, you know, just for example, customer care.
Maya (18m 47s):
So it doesn't really matter what specific CRM using, as long as we tested those core skills that each and every common customer care representative was, is required to do.
Joel (18m 58s):
Let's talk about diversity for a second. And you guys obviously TA your product is everyone can take the tests. It has no mat, no bearing on your age, your sex, your color, et cetera. Talk about the product in terms of helping you recruit a diverse workforce. And at what, at some point, these folks have to get in front of an actual person where bias can come into play. Talk about that.
Maya (19m 23s):
So thank you so much for raising that because this topic is really meaningful for us. We are putting effort in diversity hiring. We have experienced in this particular space, and we also see that the companies are really interested in that space, so in terms of diversity, first of all, the fact that people can be tested based on their skills alone. This is the game changer for people with come from, you know, the various background or other representative relations, because they do not need to enter, as you said, need such as just a matter of sex or gender or gender or where they come from, or, but also, you know, those people have gaps in their CVs.
Maya (20m 6s):
Their credential are not full sometimes. So the fact that we only focus on that is as a great enabler for that. And we also put a lot of effort in working to partner with the NGOs and program that supports people that come from diverse background and implement our to inside their organization in order to pull this unique candidates into our pool of candidates. And the fact that the way we do it is not only focusing on skills, as I said, we also make sure that we are accessible. We ask people by the end of the simulation, if there's any specific accommodation or environmental factors that you want to highlight in terms of, you know, what will be meaningful for them will help them succeed in the job or in a training so we collect that as well.
Joel (21m 0s):
Touch on accessible for a second, because we did an interview recently with someone who was, you know, seeing impaired.
Chad (21m 7s):
Joel (21m 7s):
Yeah. So does your testing solution sort of adhere to accessibility restrictions and do people with disabilities have a problem with the tests and how do you sort of conquer that or solve that?
Maya (21m 23s):
Talking and consulting with the accessibility expert to be able to have an opportunity to each and every person out there, no matter what is this disability to enjoy our part. So we do have a lot of work there, but we, right now, we are accessible for most of the population. We are using pretty simple tasks. We are using accessibility factor into our platform. We do training and provide extra data for the specific people in their organization where they, you know, get service in order to help them and get, you know, a more detailed process about how to use our simulation.
Maya (22m 4s):
But I must say at the end, we will test. We are committed to make sure that we source people and no, we provide companies with the best people to do the job. So we do see in companies working with us share that they do meet different crowd of people. We are working with Ascendant. This is one of the more, one of the biggest warehouse are companies in the US and they're all in about diversity and we're putting extra effort together. And also this is not just sourcing. We are working with a team leaders in order to make sure that the manager they will meet in the end. As you said a minute ago, this is natural about sourcing, they need to meet people that can interview them.
Maya (22m 49s):
That can be sensitive to the unique needs. So we are doing an end to end process with company in that specific space.
Chad (22m 57s):
Okay. So let's pivot real quick because there are a lot of warm and fuzzy, soft skills platforms that are out there where, you know, I think plyometrics, and I think that more than plyometrics, but they have like the balloon popping game, which again, I have no clue how that is going to let me know whether, you know, whether I'm going to be a great salesperson or not. But first and foremost, what are your thoughts on those platforms? And are those really just a product of a shitty system that we've had for decades? Give me a little bit more about those types of assessments, because they're all over the place and they seem to be getting money as well.
Maya (23m 40s):
No, I think this is the problem. We are all trying to find solutions that will help us better understand our candidates. For my personal opinion and I must say, I am NBTI certificate. I know how to do personality tests. I did it three years. I do not believe in that. I believe that the conversation a minute ago, this is absolutely not accessible. And I think that for us, as an HR tech experts, we need to make sure that we do not just sell products, but also provide value to both sides. And for me, as a job seeker, doing as he said, a balloon test, it's not providing value. This is not respectful for me.
Chad (24m 22s):
It sounds like you're not a big fan of the balloon test either. Is that what I'm hearing?
Maya (24m 28s):
Between the lines?
Joel (24m 29s):
Well, I met a company in 2003 that did something similar to this and Chad and I have been around for quite a while. And I've seen companies sort of come and go with varying degrees of success, but there hasn't been the big win, right? There's no public company that does this on mass scale. It just hasn't quite happened yet. Is that a question of timing? Is this thing just too difficult to understand for buyers? Like why hasn't a product like this sort of hit big yet?
Maya (25m 3s):
You know, we are dealing with that question, my answer. And again, you know, I'm not the expert in the market like you do. I'm three years interviewing street. I believe that there's couple of reasons for that. First of all, this is why we chose not to be another assessment company. And we took the sourcing, you know, part as well. And our vision is to create a new job matching startup, not just create an ROI tool. And I think this is one of the problems. So a lot of solutions out there look at, you know, one aspect of the market and do not look at the old picture.
Maya (25m 46s):
I believe we do. Hopefully I will be with Skillset will be one of the, there are great companies out there right now. And hopefully we will be between them and, you know, scale up. I believe that when you look at the companies, the last decade that did create change in other industries, those are, people that brought new perspective and sometimes a combination of, you know, what brought models from other markets, like marketing, like, FinTech, like, you know, I think the combination of industries is that will count and a much more realistic approach and not fragmented approach that we work with all the process.
Joel (26m 33s):
Well, speaking of different viewpoints, one, I was really hoping you were going to say VR and metaverse, we're going to make it successful.
Chad (26m 42s):
Joel (26m 42s):
It's all spinning into this is VR. And the metaverse part of your roadmap in terms of what you'll be offering in the future with testing. And if not, why not?
Maya (26m 52s):
We started sourcing. We will not be able to, and we can to walk also inside, you know, influence the life of the employees. So to do retraining and 12 people find themselves inside the organization. Imagine, you know that you're a applicant will leave you within six months and after three months you provide them with a Skillset test or now a great opportunity inside the company or a couple of them. And you keep him inside because you know, he's a great talent that you can scale inside the company. So we are looking at not out in VR, but you know, let's see?
Joel (27m 33s):
It's something. I love it. I love it.
Chad (27m 36s):
All right, everybody. That's Maya Huber. She's the co-founder and CEO at Skillset. We call her Doc PhD. Maya, thanks for coming on the show. We appreciate it.
Maya (27m 49s):
Thank you so much, guys. Thank you for having me.
Joel (27m 51s):
And for those that want to know more about your company, where do they go?
Maya (27m 56s):
They can follow us on LinkedIn Skillset, or go to our website that's skillsetonline.com Have come looking for to you to see or hear from you guys.
Joel (28m 6s):
Love it. Chad, another one in the can, baby.
Chad and Cheese (28m 10s):
OUTRO (29m 4s):
Thank you for listening to, what's it called? The podcast with Chad, the Cheese. Brilliant. They talk about recruiting. They talk about technology, but most of all, they talk about nothing. Just a lot of Shout Outs of people, you don't even know and yet you're listening. It's incredible. And not one word about cheese, not one cheddar, blue, nacho, pepper jack, Swiss. So many cheeses and not one word. So weird. Any hoo be sure to subscribe today on iTunes, Spotify, Google play, or wherever you listen to your podcasts, that way you won't miss an episode. And while you're at it, visit www.chadcheese.com just don't expect to find any recipes for grilled cheese. Is so weird. We out.