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HRx Radio – Executive Conversations: On Friday mornings, John Sumser interviews key executives from around the industry. The conversation covers what makes the executive tick and what makes their company great.

HRx Radio – Executive Conversations

Guest: Liz Wessel, Co-founder and CEO WayUp
Episode: 352
Air Date: February 7, 2020




Important: Our transcripts at HRExaminer are AI-powered (and fairly accurate) but there are still instances where the robots get confused and make errors. Please expect some inaccuracies as you read through the text of this conversation. Thank you for your understanding.

Full Transcript with timecode

John Sumser 0:13
Good morning and welcome to HR Examiner’s Executive Conversations. Today we’re going to be talking with Liz Wessel, who is co-founder and CEO of WayUp, an early career platform for helping professionals get hired. How are you?

Liz Wessel 0:27
I’m great. Thank you, I feel like I could listen to your intro music all morning.

John Sumser 0:33
You know, I’m about 700 shows into this process and so I still like it, which is kind of amazing. Yeah, yeah. So would you take a moment and introduce yourself?

Liz Wessel 0:44
Absolutely, so I’m Liz Wessel. I’m a co founder and CEO at WayUp. We are a tech startup based in New York City that help employers locate fantastic, qualified, diverse, early career talent ,and I’m sure today we’ll be talking about how we do that.

John Sumser 1:00
Yeah, that’s great. So how did you end up doing this? I can’t imagine you were five hanging around the sandbox in kindergarten going, man, what I want to do is run a tech project that helps people get jobs. So you stumbled on this somehow? What’s the story?

Liz Wessel 1:18
You are totally right. I think at that point, I wanted to be either a Spice Girl or the mayor of New York City, but definitely not a startup CEO who was working in HR Tech. So you’re right about that. At a high level, I was in college at the University of Pennsylvania with my co-founders, JJ, who’s our technology officer also at Penn.

Liz Wessel 1:37
And we saw firsthand from the early career candidate perspective, just how awful the job search is, because these companies are recruiting balls at volume, you know, at scale, and yet, they’re not recruiting people who have no skills or who have high turnover. It’s not like a cashier job or I thought this is jobs like engineering roles and sales roles and marketing. roles and so on. We just saw that there was a mismatch of how candidates wanted to be recruited and learn about jobs and how companies were able to recruit, again at the volume that they needed to recruit and with the diversity goals they wanted to hit. So we both had these awful experiences of looking for jobs. But we’ve landed in fantastic job working at Google, JJ at McKinsey, but we kept in touch over the years working on the respective companies, and continued to see actually from the employer side while we work in HR, help a file with various recruitment activities, and we saw just how much work and how inefficient recruiting and a lot of ways was, and so we decided to leave our jobs and start way up. And here we are today, five and a half years later.

John Sumser 2:41
Sure you built this company, what do you do?

Liz Wessel 2:44
So WayUp partners with companies to recruit qualified, diverse, early career candidates, and when I say early career, I’m saying people who have no to five years of work experience so typically college students or recent graduates out of college and won’t be Do this is we offer flexible solutions to help these employers for sunscreen for all of their roles. So for example, and by the way, all of that is focused on achieving diversity goals providing actionable insights through data and giving all candidates and exceptional experience. So just to put that into kind of context, we have a consumer side of our platform and a b2b side of it an employer side of our business. And on the consumer side, we have this point about five and a half million users who are recent grads are college students are using the app to look for jobs, get advice, and so on. And so they’ll apply for jobs. And then we actually will manage the very top of the funnel for our employer clients, which means we’ll help source for them and we’ll make sure we’re sourcing with a focus on diversity and hitting their diversity goals will screen every single candidate there are digital tools that if the client wants, it will actually phone screen every single candidate will give coaching and feedback on soft skills to the candidates. And then we move the candidate along through the company’s rest of their process and the whole time we’re giving them data on things like whether one of their steps In our process has a bias and against you and how the data shows that and what our recommendations are, and so on and so forth.

John Sumser 4:06
That’s interesting. So let’s talk a little bit about how you find that bias. Yeah, it’s a topic of great interest to me.

Liz Wessel 4:12
Yeah. So essentially what we’re able to do and we only start presenting the results to a client after having significant enough sample size, but the good thing about being an early career recruitment as most of our company’s clients who are, you know, employers that are hiring hundreds, or at least let’s say 50 to 15,000 people, the sample size of absences, usually large enough to really have great statistics. So what we do is we’ll look at what percentage of candidates of each race or gender going into each stage of their funnel and then what percentage quote unquote, come out or let me give you an example. There is a very popular engineering assessment that is very, very commonly used for campus recruiting. And we have now seen with a sample size of several thousand people across several different clients that candidates who are black and Hispanic And who takes this engineering assessment are about six x less likely to pass than white and Asian candidates now in no way do I believe that candidates of a certain race are any better or worse than engineering. However, when you start to think about it, this assessment actually is taught very popularly I’ve had to take the assessment, there are classes taught about it at top universities. And as I’m sure you’re aware of the statistics of academic staff in this country, students who go to schools that have more funding and therefore can bring in speakers to teach people how to take a test, like this assessment are going to be more likely to pass the assessment and so you end up not necessarily assessing people based on how strong of an engineer they are, you end up with less people based on how well they’re taught to take this test. And unfortunately, candidates who are higher income backgrounds, which typically are less likely to be black and Hispanic candidates end up going to those schools that teach this test. So that’s an example of where we will give to a company a company will come to us and say we’re not hiring any Boston spanic engineers. We don’t know why. So we need Just hundreds of them. And we think happy to do that. But let us also look at your funnel because we don’t want to just send you 300 qualified, fantastic candidates color black and Hispanic candidates were software engineers, you have all of the qualifications you need for you to just scale all of them. And that’s like a piece of data we would look at. I can give you one other really fun one if you’d like. That’s more. Okay, so this is one that everyone can kind of relate to. But do you remember if you had a strong GPA in college?

John Sumser 6:30
I did that I think that has to do with a strong GPA and college is not something they understand very well.

Liz Wessel 6:37
Okay, so you’ll probably then potentially resonate with what I’m about to describe, but most employers I speak to, I’ll say to them, you know, do you have GPA requirements? And very often, they say yes. And when you asked why they always say, Well, I know GPA isn’t everything, but we need some kind of qualification to be able to just roll out all the high volume we have and while that’s totally fair, that understand that They want some kind of balls. To do that. I always recommend rep GPA is not the one and here’s why. So most employers have preferred I certainly one of them would prefer a student with a 3.0 or 2.5 job versus a student with a 3.3. Plus their time in the library, and yet campus recruiter as well. Awesome. Hi, Allison hi have like a 3.2 of the most popular one. And so we did the data, we pulled the data and we looked at students who attend four year institutions, and we looked at them at junior year, which is a very popular year to be applying for internships. And we saw that the average Asian student has a 3.3 GPA, the average white student or 3.2, the average black student, a two point, the average Hispanic student a 3.0, and so on and so forth. So when you think about why that is, it’s not it’s not about people of one race or another being any smarter or less smart than another Georgetown University with a really interesting study that showed that GPA is very often correlated to how much time you’re statistically spending in the library. And so on.

And students have lower economic, lower income families are more likely to need to work more during school, which means spend less time in the library. And as you can imagine, unfortunately, that disproportionately going to relate to black and Hispanic students. And so this whole concept of having a GPA requirement as a brilliant backfiring and companies that are trying to optimize for more diversity into and through their funnel. So that’s just another example of a requirement that is at the very top of funnel that so many companies have, and yet they don’t realize that there are so many other better qualifications you could assess candidates for. So that’s the kind of thing way up does we look at every one of your requirements, the questions, you’re asking candidates, what you’re ruling them out for, and we are able to provide those data insights to our clients that they can change very, very small tweaks to their processor requirements, which can sometimes triple or even quadruple the number of female candidates. They’re hiring people of color and so on.

John Sumser 8:54
This is interesting. This is a story I’m starting to hear over and over again that the answer To the discovery of diverse candidates is to look more deeply into your own pipeline, rather than throw up your hands and say there aren’t any. And that sounds like what you do. I’m fascinated by the idea that GPA is really a reflection of social class, which is what you said.

Liz Wessel 9:17
Not always. But statistically, that is what data does show. And by the way, if you’re hearing that more and more, because I very rarely am hearing heads of ta or HR saying, I’m starting my hands up and giving up and stuff, they’re just spending all this money on more sourcing tools so that they can proactively reach out to you know, female engineers or black insurance on or they’re spending all this money to go to all these conferences, when it’s like a lot of these people are already applying for your jobs. You’re just overlooking them.

John Sumser 9:46
So one of the other things that I’m hearing I tend to hear the events part of the market that’s that’s where I work is that search technology never really made it to the HR tech universe and the recruiting universe in particular, so the idea that one must write horribly complex Boolean search strings to be able to use Google to find stuff is kind of different than everybody else’s experience or search, which is you go to the search engine, you enter a term and it gives you a result that you like. And so there are companies who are adjacent to you who are doing things about the search aspects of the thing that you’re talking about, rather than the credential aspects. I applaud the work. So how many customers do you have? And how successful Are you are persuading the search of things, right, because it’s to teach what you’re trying to teach.

Liz Wessel 10:43
Sure, I’ll answer that. One quick response I have on the search is just we’re definitely not experts in AI search or machine learning search. But one example that I can share of how an interesting insight we heard was that when it comes to job recommendations, we used to always focus on Where the student tells us a recent grad tells us they’re looking for a job. And so we would really focus our job recommendations on that location, because it’s not they want to look for a job in New York. And so we want to show them jobs in New York. But what was fascinating was over time and time again, let’s see the data showing students, they they want to work in New York, but if they get their dream job, they’ll move to Mountain View, they’ll move to Boston, they’ll move somewhere else. And so it doesn’t always happen. And you know, it definitely is more for that dream job, quote, unquote, than it happened for media as a company that’s not necessarily in a place where they can offer the dream salary or the dream job opportunity. But it is really interesting with respect to what we present to our candidates, sometimes what they tell us they’re looking to see is not necessarily what they’re actually seeing. So we’ve been learning that respect to look at, well, where are you actually applying for roles? And what are the things what are the qualities along those roles that you’re looking at and that they have in common so that we can recommend jobs based on that so I definitely hear you and I’m not an expert on search and AI and search but it very much resonates what you’re saying.

John Sumser 12:01
I like what you just said, the really interesting thing is that all of the dating apps know what you just told me, which is that people say what people say they want. And what they actually want are two different things. And if you’re going to deliver results that are useful, you pay attention to what they do not do what they say.

Liz Wessel 12:23
Yeah, exactly. I was on a, I was talking to someone who was the CEO of the largest, or one of the two or three largest dating apps in the country around the world. And I remember they were just saying on dating apps, our biggest lesson is this always show the hot blonde first. Blonde, so can I don’t take offense to this? I remember they said that to me. And I was like, wow, what is the parallel in job search? And is that how people do it? Is that how bigger companies to show whatever the equivalent of the hot blonde is first, you know, that Google job or whatever it might be? So it is really interesting, and I’m not an expert on that at all.

John Sumser 13:00
That’s and it’s great that you’re thinking about that stuff. So yeah, in your work, you deal with an extremely important ethical issue, but it is a broadly misunderstood question, and one that is subject to a lot of confusion, you know, because when you’re trying to tackle bias inside of a system, it’s easy for people to get defensive about the question. So you must encounter that in your How do you deal with that?

Liz Wessel 13:25
Yeah. And I also want to answer your prior question as well. Before that, I don’t want to ignore it. Yes. You know, how big are we how many companies we work with? The short answer is we work with thousands of employers, massive companies that are fortune 100. And whether it’s Unilever started fortune 500, Unilever, Starbucks, Citigroup, Deutsche Bank, and so on and so forth. All Verizon all the way to much smaller companies that are startups all the way to, you know, companies in the middle, we have nonprofits, etc. So we definitely work with companies of all sizes, but I will say it’s been really rewarding as we went from once upon a time we were just the story platforms. And we kept seeing the problems that I just described where the candidates who we sent to our clients who were exactly what they were asking for would not get through their funnel. So that’s why we ended up adding another component, which is the screening component where our clients, which is just resulted in such higher diversity metrics and results. So anyway, the short of it is with that product, we really focused more so on this mid market and enterprise world because you know, it definitely can really help lower cost, improve ROI and help supplement a strap team who just is not enough people to handle the volume that they need scale and scale down to occur. So that’s the short event happy to answer any of your questions about growth after and I’m so sorry, would you mind just reminding me the question you just asked? It was a great one.

John Sumser 14:42
Yeah. So you are in the business of correcting bias and systems and when you correct bias in the system, it’s inevitable that people take it personally and find themselves accused of being bias and I’ve never seen anybody take that gracefully. Talk a little bit about how you’ve learned to navigate the dynamics of those conversations?

Liz Wessel 15:05
That is a fantastic question. So I will say, it’s never our opinion, we’re just going with data. So the good news is that they can’t get mad at us for Oh, you think I’m biased? Because we would never say you are bias, we simply show them, hey, we just want to show you that the pass rate for men in this stage of your assessment or at this stage of your interviewing is 80%. And the pass rate for women is 10%. And the sample size is statistically significant. And then we usually end that with what is the takeaway from this? What are your thoughts about it? What should the action I’ve often done implementing the action items? For example, it might be the hiring managers need to go through a bias training it might be they realized that their higher view is completely I mean, this is definitely a common one that like a video interviewing tool. Definitely we have time and time again, biases against specific growth, even not just from who passes but even from whom oxen to even completing it. And so I absolutely am pleased to say We never have to give opinions and say we think your bias, we’re just showing data. Now I will say On the flip side, I’m going to give you a side by side of what companies and so how some companies react one of our clients has an engineering assessment. So one that’s very popular. And we showed them the data. And we showed them how their black and Hispanic candidates were failing at such higher rate, which almost every company we’ve seen, who uses this assessment fees, and they said, What are you, you know, thinking of doing? And they said, well, let’s just focus on women. And I was like, so you’re just not at all going to, you know, optimize and change your funnel to react to the fact that entire groups of races and ethnicities are not getting through your funnel. And they said, well, let’s just focus on women. Though, you know, sometimes you see companies just simply don’t care. But we always try to make sure that we’re making them as little as possible. And hopefully, they’re aware of the laws and we just have to hope that our clients can always make the legally spouses and best judgments and whenever we see something that they are doing, and I’ll give you an example of something we see that they’re doing that is not legal, we will flag it to them immediately and we will absolutely not work with a company who continues to do it. So there is fortune 50 company that we had once worked with that told us anyone whose name sounds Asia, I quote, we reject because they’re so likely to hire this company said they’re so likely to have a visa requirement and we don’t offer visa, and we said that is like blatantly racist and that stereotyping and that very illegal, and we even provided links and so on. And they continue to do that. And so we don’t work with them anymore. And now they work with a quote, unquote, you know, competitive company to us and are doing the same practice with them. And, you know, it is what it is, and so definitely see awful things. But we do our best to make sure that the 99% of companies who actually want to get better are able to get better, unfortunately, really is more like 99% versus be the opposite.

John Sumser 17:38
That’s interesting. You know, given the political environment, one would be tempted to think that it was more than 99%. But what you’re saying is that a lot of the bias that’s in systems is deeply unconscious, and the people who are militant about wanting to retain their bias are besides stupid in a video Very rarefied minority. So now you’re talking about a group of people who wouldn’t you encounter their systems, you see bias, do they just change because you show them data, that would be an interesting thing to believe.

Liz Wessel 18:14
I can tell you that there are so many companies that have made immediate changes, companies that have dropped their GPA requirements on the spot companies that have decided, you know what, let’s lower internet. This is a few of our companies this year lowered intern salaries by you know, something like $1 an hour, whatever it was, it was a meaningful amount. But in order to give that money upfront to those companies to be able to give sorry to those candidates, to be able to give them relocation stipends and say, Hey, we know that you might not be able to pay for your flight to start the process. So instead of paying a little higher salary will lower that balance a little bit and take that money and pay for your flights in the first month of rent and so on so forth. So we’ve seen that the data we give to companies actually has resulted in actions and that is why it’s coming to work. doing what I do, despite the harder days because we see that fantastic candidates are getting hired and that companies are making changes in their process. I will tell you if we were just providing data and then having companies say, Okay, now we have the data, we’re not going to change anything, I’d probably not be in the job I’m in today, because that would be a depressing world.

John Sumser 19:19
Well, that’s awesome to hear. Have you thought much about going upstream? You know, the world that you’re describing is more amenable to a gross statistical analysis. And some of your success comes with the fact that the numbers are bigger in this setting. When you go further up the career ladder, things get more deeply differentiated than the data sets are not quite as big on a per job basis. Have you thought about tackling that part of it? are you sticking to your guns?

Liz Wessel 19:49
Fantastic question. Yeah, it’s a fantastic question. I honestly don’t know the answer. Five years, this is what we do best. And so I just don’t elevate Answer. I know, and I would imagine the bias does not stop at early career, I would certainly imagine that it only could potentially only increase, but I just I really wouldn’t know you would know. So I would I would turn that around, you

John Sumser 20:14
Well, I think it’s probably worth looking at as, as I’ve learned about bias over the last five years, when I’ve started to discover something that I thought was a sort of a fixed variable. If you went into a company and there was bias there’d be 5% bias, but what’s really the case is that bias is a variable thing. And it’s more like a liquid than it’s like a solid and so rooting it out and seeing it doesn’t necessarily give way easily to big statistical analysis because it’s not a constant inside of a system. It appears at moments of stress. It’s it’s a dynamic variable rather than a static variable. And so I’m always interested Starting with good data that shows bias and then figuring out how to chase it down when you get to the more sophisticated problems where it moves faster like a cockroach, than it moves like a paperweight or something, which is how we think about it. And so it’s a subject of great interest to me, as I always asked.

Liz Wessel 21:21
Yeah, I will say to your point, I’m not an expert on this. But one, one thing that reminds me of is I do believe that you’re right, it’s likely that there’s too small of a sample size to necessarily use data by company. But when you look across companies, for example, percentage of females who are CEOs of Fortune 500 levels don’t often start data. So clear insights. So there’s really great organizations like one is called CLR for diversity inclusion, and I’m a part of that organization. It’s at Studio I think I want to say they must be classified as a nonprofit but they’re an organization that PWC started and they basically are former CEO’s from all five companies in a way up as a small

company have under 100 employees all the way 50 goes with companies like PwC, and so on, like massive, massive companies. And they all get together once again and have smaller group meetings, the year about what they gather, and hopefully the you know, some of the actions are happening across companies, because that’s one of the only ways to create a large sample size.

John Sumser 22:29
How interesting. So I’m so excited about your work and this has been a great conversation. Would you mind reintroducing yourself and telling people how they might get a hold of you?

Liz Wessel 22:39
Absolutely. So once again, I’m Liz Wessel. I’m the co-founder and CEO at WayUP, we’d love to be in touch with you. So if you’d like to check us out, you can go to WayUp dot com, that’s w a y u p dot com, like not way down, way up. And if you want to get in touch with us, go ahead and email us at engage at E N G A G E which because you’re engaging with us at and if you want to follow me, follow us on twitter @ WayUp or me is at @LizWessel.

John Sumser 23:14
Thanks Liz, this has been a great conversation. We’ve been talking with Liz Wessel., who is the CEO and co-founder of WayUp. What I didn’t tell you in the beginning is let me give you this list. This is amazing. She’s a TEDx speaker, Forbes 30 under 30. Advertising Week, TechCrunch Disrupt, South by Southwest nace and notable events, has some history at Google as a manager in California and India, so you can expect to see more from her as time goes on.

Liz Wessel 23:44
Thank you so much.

John Sumser 23:44
It was great talking with you, Liz. And thanks, everybody for listening in. Yep, we’ll talk to you next week. You’ve been listening to HR Examiner’s Executive Conversations.

Bye Bye now.

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