PAW 2019 Podcast Series

PAW 2019 Conference Series – Matt Cowell – QuantHub

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Matt Cowell, CEO of QuantHub.

Find Matt Online:

Email: mcowell@quanthub.com

LinkedIn

QuantHub


[00:02]

Hi, I’m Jamin.  You’re listening to the Happy Market Research Podcast.  My guest today is Matt Cowell, CEO of QuantHub. Matt, I’m really excited about this conversation we’re going to have.

[00:13]  

Yeah, yeah.  Thanks for having me on.  I appreciate it.

[00:14]  

So, you guys have a super interesting product concept or business that I am thrilled to dive into.  It looks like (now, keep in mind I haven’t spoken to anybody but just kind of walking to the booth and looking at your video) it looks like you are a… almost fit inside of the HR layer for data scientists.  Is that kind of…? 

[00:42]  

Yeah, that’s correct.  Really data scientists, data engineers, data analysts, all advanced analytics professionals. 

[00:48]  

Got it, got it.  Well, I should be quiet.  Why don’t you tell us a little bit about your business.

[00:52]

We actually help companies that are hiring in that area, whether it’s data science or data engineering.  These roles are really popular right now; they’re really hot. Everyone wants to be in this field. And so, what’s happening in the industry is when people post a position, they’re getting a ton of candidates.  And so, we found that people are spending just an inordinate amount of time doing the technical screening for those candidates. So they’re taking their most valuable resources and they’re having them do interviews all day long.  

[01:18]

Which is insane.

[01:19]   

Yeah, which is crazy, right?  So, there’s a shortage of these people.  And the ones that they do have are actually spending all day interviewing.  And so, we have this problem ourselves. We’re actually a spin-out of a data science consulting company.  And so we built a platform to do…  

[01:32]

I don’t know what’s happening behind you but…  It’s like Lord of the Rings

[01:36]  

It’s like I’m dropping the mic right now.  I’ve got sound effects on the podcast. It’s incredible.  And so…  

[01:44]

Wait, wait for it, wait for it.  My God, what is happening in there?

[01:50] 

Incredible timing, incredible timing.  

[01:52]

Literally, the whole time this has never happened.

[01:54]

This is amazing.

[01:55]    

OK.

[01:56]

OK.  And so, we actually built a skill testing and data challenge platform for these roles.  In the interview process, what would happen is you would actually get candidates that would probably come in by the hundreds because the job’s very hot.  And then, you would send them some sort of skill test in machine learning or data wrangling, data exploration or R, Python, you know, all the kind of relevant skills, depending on the job; and use that to sort of screen out people.  And then you might also send data challenges that are more hands-on.     

[02:26]

So, those exercises, are they done remote or are they done on-site? 

[02:31]  

Yeah, they’re web-based.  They’re all web-based exercises.  They’re all graded automatically. And so, it takes about two minutes for a HR person or a tech lead to send out these challenges.  And then, they just start getting scores in from candidates.

[02:45]

I think it’s fascinating.  And it looked like there’s a score card of your overall quality of talent or talent quality, sorry.  That I thought was really interesting because it identifies potential weak spots that you need to create a bridge for from a training perspective.

[03:04]

Right.  So, you’ll see companies out there that do assessments like we do.  And so, I think what’s different about us is we don’t just do that for hiring.  We want to apply that to the entire really talent process. So, bringing on people, of course, that’s a normal use case.  People use assessments, but also to your existing team. We want to help benchmark the skills of your team, help people figure out sort of where they want to go in their careers and actually that sort of skill gap that they have to get there, and then align them with training.  So, if you’re a leader of a team, you could see that sort of skill graph and see where you’re deficient. You can work with your individual team members and figure out where they want to go and then what skills they need to get there and then align them with training. The problem we’re trying to solve there is there’s so much training; there’s so many companies now focused on training in this field that it’s a little overwhelming.  People don’t know where to start. And so, what we’re trying to do is help them actually make it much more targeted. And so, I don’t want to go to an intro to data science course. Maybe I just need to learn NLP. And so, let’s actually determine that through assessments and benchmarking.    

[04:04]

What’s interesting is at scale, all of a sudden, you can be like…  Part of the meta here is you could become the greater of the data science camps that are proliferating everywhere, right?  You could say these guys are generating… You know this is their score. 

[4:18] 

It’s a normal evolution of content.  So, if you think about a company like Trip Advisor, why do they exist?  They exist because there’s so much content out there that someone needed to come in and aggregate and point people in the right direction.  And I feel like that’s exactly where we’re going to fit. There’s so much training content someone needs to come in as an aggregator and point people in the right direction.  And that’s kind of where we’ll fit.    

[04:41]

That’s a fun thing too from an SEO perspective.  You really have an opportunity to differentiate yourself and then jump materially in the rankings, which isn’t bad either.  

[04:53]

That’s the hope.

[04:54]

That’s right.  That’s interesting.  So, you’ve been part of the industry for a while.

[04:58]   

Yeah, I’ve been in product and tech for a long time and a little bit in analytics.  So, in the last couple of years now, I’ve been exclusively focused on the analytics space, which has been fun ‘cause it’s pretty hot.    

[05:09]

Yeah, I love it.  When you look forward in the next three to five years, what do you see as a trend?

[05:15]      

Yeah, I think people get worried about jobs being automated and gotten rid of and displaced, I should say, by automation.  What I think is interesting about this field is the role of data scientist is not something that is easily automated. You know there’s all these companies, even here at this conference, that are doing automated-type model-building and whatnot, but the actual data science part of it is pretty tough.  That’s pretty tough to automate. So I think that will continue to evolve and become more and more something that companies just have to invest in. So I think we’re at a great point here where all companies are doing something in this area. And so, for us, I feel like we’re at a good place in the industry as it continues to evolve and people invest more and more in this.  So I feel like that’s an interesting… It’s an interesting time.

[06:09]

Observationally, it feels a lot like kind of the web as the web started to scale up:  kind of mid-Yahoo. I’m so old I have that point of reference, right?

[06:20]

Me too.

[06:21]  

Which is exciting for me because you can really see how this thing is going to J-curve.

[06:26]  

Yeah, yeah, definitely.

[06:27]  

Everybody knows they need to do this:  employ ML or AI, etc. into their overall business processes and decision-making.  But they’re not exactly sure how or where, but at the same time they’re willing to sequester big pieces of corporate budgets in order to solve that problem.  So it’s a nice land-grab opportunity. To your earlier point, the ones that are adding value like the data camps or whatever and creating great outcomes for the businesses, I think there’s mega opportunities over the next five years.   

[07:04]

Yeah, another thing, I think, is happening or will happen is my background is more on the product and tech side within software development.  I gave a talk yesterday that there are things that we do in that field: embracing product management, agile and lean concepts that, for some reason, we’re not yet really embracing as much in the analytics space.  So I think that will continue to evolve. We’ll learn from our sister fields and I think that’s going to help us deliver better outcomes. I think right now we’re all excited about the cool tech but not always delivering on the outcome.  So I think we need to align better with the business strategy, which is not straightforward to do, but there are roles out there that – product management being one of them – that, if we embrace that, I think we’ll have a better shot.     

[7:48] 

Yeah, totally.  One of the CEO’s that I’ve interviewed had this term “data diversity.”  And I thought that was creating better outcomes for businesses. That’s not like single source; it’s looking across the organization in the market in order to find out whether it’s consumer data or employee data or what have you.  So the more you that you can create like benchmarks, third-party validations and then internal… You know start triangulating this point of truth. 

[08:14]  

Yeah, yeah, that makes sense.

[08:16]  

Uh, what do you think about the show?

[08:19]  

I think it’s good.  This is our first time out here at this show in Vegas, and it’s been good.  What’s interesting, as an exhibitor, is that there’s no traffic when sessions are going on, which actually must mean that it’s really good content.  Normally, people don’t go to all the sessions like this. So it’s like a ghost town here during sessions.   

[08:39]

Yeah, it really is.

[08:40]

Because of all the special effects behind me that you just heard.

[08:43]   

I think they just did that for you.  That’s hilarious.  

[08:48]

But it’s been good.

[08:49]  

It’s been good.  I like how they’ve laid it out.  I think there’s lots of good cross-pollination in between the speaking sessions.  And the food’s been pretty good. 

[08:57]

The food is exceptional.  I go to a lot of these shows, and the food here is exceptional.   

[09:02] 

Me too.

[09:04]

Yeah, I’m going to need to work out when I get home.

[09:05]

I know.  

[09:07]    

So, if somebody wants to get in contact with you or sales at QuantHub, how would they do that?

[09:12]

Well, you can go to QuantHub.com and we’re offering a free trial.  You can email sales@QuantHub.com, and we’ll be right back in touch.  And so, easy to get in touch with us.  And it’s super easy to get started. We work with companies that are testing and assessing 5,000 people a month and startup companies that are doing 5 a month.  

[09:32]

So you got a nice spectrum there.

[09:34]  

It’s been a little interesting how it’s played out that way, but it’s really easy to get started.  If you’re not sure how many candidates coming in a month, then you could still get started really inexpensively.  

[09:44]

Do you guys also, you know…  Thinking about it… I imagine you’re having some visibility where the prospects, candidates are getting sourced from.  Do you see that as something that as a service or auxiliary partnership?   

[10:01]

Yeah, so for us, actually that is an area that we’re just now starting to get into.  We’re doing public challenges, “public” meaning in the data science and the data engineering community.  And so, what we want to build up is actually a community of people that go through these challenges and actually vet their skills and help them benchmark where they are but then also align them with people, companies that are looking to hire people like them.  And so, I think that’ll be an interesting opportunity. Obviously, we’re not doing that with the database of people coming from a company but, if we’re doing that with public challenges and whatnot, then we can help on the sourcing side, which is a pain point that companies have as well.  They’re not able to find all the best people. And so, we want to help with sourcing and vetting. Right now, we’re more on the vetting end and development; we also want to help on the sourcing side.  

[10:49]

Yeah, makes a lot of sense actually.  I think more and more I’m seeing single source being a direction that businesses are going to right now.  So the more you can offer the complete package, the simpler their lives are.

[11:01] 

Absolutely, yeah, without a doubt.  We’re very niche-focused; we exclusively focused in advanced analytics.  We think this field has neat characteristics and there’s plenty of room to run here.  And so, we’re not planning on getting out into other spaces. We want to get in this space and know it really well and be excellent at this.  We came out of a data science consulting company; so, this is sort of in our DNA.

[11:24]

Totally.

[11:25]

And we think this role is different:  This is not a programming role. A lot of companies we’ve seen treat it like a programming role and really miss the statistics side and the modeling side.  And so, we really come at it from that perspective more than anything else.  

[11:38]

It’s got to be refreshing from the customer point of view.

[11:40]   

Yeah, yeah, we’ve had customer actually displace others that were more programmer-based assessments.  We test on programming, of course, but displace those companies because we were more statistically oriented.

[11:52]

Right, exactly.  I think historically that’s been overlooked, but I do think that’s getting rectified right now from a priority perspective.  

[12:00]      

Yeah, yeah, definitely, definitely.

[12:02]

So, sales@QuantHub.com.  My guest today has been Matt, the CEO.  Matt, thanks for joining me on the Happy Market Research Podcast.  

[12:09]

Yeah, it’s been a pleasure.  I appreciate it.

[12:11]  

Everybody else, if you found value in this episode, please take the time to screen shot it, share it on social media.  I really appreciate it, and I hope you have a great rest of your day.