Despite some highly publicised layoffs in the tech sector, macro trends are still driving a high demand for highly skilled tech professionals. This is particularly true in data science and AI, where hiring the right talent can have a significant business impact.
So what do employers need to do in the current market to ensure they are getting the talent they need?
My guest this week is Kevin Dewalt, CEO of Prolego, a leading AI consultant for Fortune 500 companies. Prolego has recently published a report to help companies get better at hiring data science talent, and Kevin has some valuable insights to share.
In the interview, we discuss:
• Why we are only at the beginning of a long-term trend in AI adoption and innovation
• The current state of tech hiring
• Why there is no new wave of talent in the market
• Creating the right working environment to be an employer of choice.
• Attention and attraction
• Gathering talent intelligence to understand motivations
• The critical importance of the hiring manager and why you should include them in the JD
• Longer-term structural considerations
• Prioritising learning and the importance of non-management career paths
• How to compete for talent with big tech
• The future of AI in recruiting technology
Listen to this podcast on Apple Podcasts.
Eightfold.ai (0s):
Support for this podcast comes from Eightfold.ai. Eightfold.ai delivers the talent intelligence platform in the most effective way for companies to retain top performers, upscale and rescale the workforce, recruit top talent efficiently, and reach diversity goals. Eightfold.ai’s deep learning artificial intelligence platform empowers enterprises to turn talent management into a competitive advantage.
Matt Alder (47s):
Hi there, this is Matt Alder. Welcome to Episode 450 of the Recruiting Future podcast. Despite some highly publicized layoffs in the tech sector, macro trends are still driving a high demand for highly skilled tech professionals. This is particularly true in data science and AI where hiring the right talent can have a significant business impact. What do employers need to do in the current market to ensure they’re getting the talent they need? My guest this week is Kevin DeWalt, CEO of Prolego, a leading AI consultant for Fortune 500 companies.
Matt Alder (1m 29s):
Prolego has recently published a report to help companies get better at hiring data science talent and Kevin has some valuable insights to share. Hi, Kevin, and welcome to the podcast.
Kevin DeWalt (1m 42s):
Thank you so much. Thank you for having me on this show. Been looking forward to it.
Matt Alder (1m 45s):
An absolute pleasure to have you on the show. Please, could you just introduce yourself and tell everyone what you do?
Kevin DeWalt (1m 51s):
Well, my name is Kevin DeWalt, and I’m one of the co-founders and CEO of Prolego. We are an AI consultancy that helps the Fortune 1,000 get started on their AI journey. Our mission is to make AI the default operating system of business at the world’s largest companies.
Matt Alder (2m 8s):
Fantastic stuff. That sounds like a multifaceted type of business. Tell us a little bit more about the main activities that you do. I suppose, in particular, your involvement in recruiting and talent acquisition.
Kevin DeWalt (2m 20s):
Yes, we are about five years old. As you can probably guess, AI is at the very beginning. All of ourselves, our clients, and really the whole world is at the beginning of what it’s gonna be a long term, massive trend in how computers and how businesses use computers to achieve business goals. We do everything from strategy. We have engineers to do hardcore modeling and build the AI systems, but then a big part of what we do is really help our clients change culture. Of course, changing culture involves changing people, and part of that is recruiting and retention strategies, in particular, in this area, which has become incredibly hot and so competitive over the last couple years.
Kevin DeWalt (3m 0s):
We do a lot of basically helping our clients figure out how they can attract and retain talent. That led to the workforce study that we commissioned recently on data science talent.
Matt Alder (3m 13s):
Fantastic stuff. We’re gonna talk through the report that you’ve just done shortly. I suppose, before we do, though, I’d be really interested in getting your view on the market basically. We’ve been talking for a number of years about technical skill shortages. Things got more acute in the pandemic and straight after the pandemic, but obviously, a lot of the cycle now is about layoffs in the tech industry. Well, what does the market look like and what’s the competition for talent like in these highly skilled tech jobs?
Kevin DeWalt (3m 50s):
Yes. It’s fantastic question, Matt. Well, there’s a couple of large forces and then some temporary forces at work. On a global scale, and having listened to some of your previous guests, I know you’re well aware of this, but I thought I would just mention it for the sake of the audience. Of course, we have a massive demographic shift happening globally. The baby boomer generation is the biggest in history. They’re going into retirement. Generation X in the United States is much smaller. Of course, globally, there’s going to be a labor shortage across the world. We have that global headwind happening, and the pandemic, of course, accelerated a lot of that. We have that macro shift, but at the same time, there’s are a lot of traditional industries that have trying to go digital.
Kevin DeWalt (4m 37s):
You hear like digital transformation across insurance and banking, financial services, transportation, and retail that’s happening everywhere. For the past couple years, there’s this really this bow wave initially driven by the tech companies, but lots of other companies are trying to catch up. They’re looking at expanded technology workforce. In the past six months or so, we’ve seen a lot of the higher profile tech companies, some of the larger ones and some companies that have raised a lot of money that really got ahead of their skis in terms of the number of talent they were trying to attract. They were beating each other up, competing like crazy. In many cases, lowering some of their traditional standards just because they were in a war for talent.
Kevin DeWalt (5m 19s):
We’re now seeing some higher profile, layouts happening at some of the larger tech companies that I’m sure your audience is familiar with, but it doesn’t change the macro trend. My co-founder and I have been trying to figure out really what’s happening on the ground. We’ve been talking to colleagues, C-level executives, people that we know, our clients about what’s happening in the workforce. Truthfully, nothing has really lightened up. Although there’s high profile layoffs, it’s happening at the same time that a lot of traditional industries are expanding or hiring. Banks announcing five to 10,000 new hires for their digital transformation, so the larger trends are happening.
Kevin DeWalt (5m 59s):
We really haven’t seen a lead up. Companies are still having a very hard time attracting AI talent. The best tech companies are still recruiting and hiring, although maybe some of that has slowed down. Unfortunately, we occasionally will talk to companies who are saying, “Well, we’re hopeful that the market’s gonna turn around and we’re gonna be able to have a wave of new talent coming in and we won’t have to do things any differently in the next six months.” Usually, I have to tell them that that’s unlikely based on what we’re seeing.
Matt Alder (6m 27s):
No, absolutely. I think that really echoes a lot of the other conversations that I’ve had on the podcast. The demographic trends are there, the digital transformation trends are there. I suppose, the current reality just varies from company to company, but the general direction is the same.
Kevin DeWalt (6m 42s):
It is, yes.
Matt Alder (6m 44s):
With that in mind, I was really interested to read your report on how to win the war for data science talent in particular. Why did you write the report? How did you do it? Tell us about the background. Tell us about the background to it.
Kevin DeWalt (7m 0s):
We really noticed a trend in the last couple of years. Maybe for the benefit of your audience, I’ll explain what we mean by data scientists. These are people that are primarily building a lot of the AI systems that you use. You may hear of model development, AI system development, or experimentation, but it’s a new breed of career of people who use data and try to discover new business opportunities within the data. I’m sure all of your audience knows what a scientist does, experimenting and trying to discover something new. Now, companies are hiring technical workforces to help discover new ways to make more money, save more money, work more efficiently, design systems that can take work traditionally done by people and maybe automate some of that so people can do more interesting and more higher level work.
Kevin DeWalt (7m 46s):
The data scientists are the people that discoverer and brains behind that. In the past couple of years, it’s been a career path that has grown tremendously fast, but the rates of competition and salary growth have really been astounding. You’re seeing anecdotal stories of people who are changing jobs two or three times over an 18 month period. Their salaries are increasing by a hundred thousand us dollars a year. It is really crazy out there how tight the market for this top talent has. It’s because there’s not only a shortage of them, but the outsized impact that some of these people can have on your business is much bigger than a traditional technology worker.
Kevin DeWalt (8m 33s):
A good software engineer can traditionally make software that maybe works more efficiently, that’s easier to maintain, that does a better job, but they’re not gonna have a 10X impact on your business. A data scientist who come in and discover a new way to make money or new way to save money can have a multimillion dollar or tens of million dollar impact on your business through a single discovery. That’s why the market for the people who can do this work has become so competitive. That’s the background. Back to your question, the motivation for the report is because as we saw this competition happening, we really saw a bifurcation in some of our clients and the companies we know who were able to attract and retain these workers and those who are really struggling.
Kevin DeWalt (9m 20s):
We had some ideas for what these best practices were, but we wanted to go out and confirm and discover what we were hearing from the market of the talent themselves and what they were saying about the type of work environment they want to work at. That was the motivation for the report.
Matt Alder (9m 39s):
I think that’s interesting because there are some aspects in the report about recruitment and attracting talent that I wanna talk about in a second. I think that first bit about the working environment that companies need to create and how this type of workforce might behave differently or have different needs from their employer, really interesting stuff. Tell us more about that.
Kevin DeWalt (10m 4s):
Yes. As far as the workforce itself, if you look at the psychology of the people that go in to become a data scientist, they’re extremely passionate about what they do. These are people who you might have grown up with who loved tracking sports statistics in Excel or a dashboard, and could tell you every quote about every, either if it’s a football, or if you’re in America, an American baseball, every stats in every player. That’s the person that goes into this kind of work. They’re just very passionate about discovery. They love looking at data, they love solving problems. That’s why they go into this career. For the most part, they really, really like that work.
Kevin DeWalt (10m 45s):
For them to be successful, they have to have the right projects. They have to have the right environment, the right boss, the right infrastructure, and the right supporting systems around them so that they can be effective and do what they want to do. If they don’t have that, they’re not gonna have the job satisfaction and they’re gonna look for something else. I know this is a theme to a lot of your interviews, but we’re entering a world where employers are no longer in control. It’s the talent that’s in control. We have to understand what they’re looking for and try to create an environment where they’re gonna be successful, even if that means kind of changing how the company is currently organized.
Matt Alder (11m 25s):
I suppose that’s interesting because, for many organizations who are rapidly expanding their data science functions, what do they have to do to be seen as an employer of choice in this area, but also, how do they communicate that to the broader data science community?
Kevin DeWalt (11m 44s):
I think the first thing is to maybe take a step back and actually have a strategy. Most companies that begin this journey, they have an existing organizational structure. Maybe they have project teams or they have their way they’re organized. The first wave of attempt is, “Okay, now we have a project team building a system. We need to add a data scientist to that team.” That’s usually how companies don’t wanna change for no reason. That’s expensive, it’s hard. Changing culture’s tough. The easiest thing for them to do is to try to add new talent to the way they work. Data scientists look a lot like software engineers. They work with software, they work with data.
Kevin DeWalt (12m 25s):
They add existing product team, maybe a product or a project team. Usually, it’ll find out there’s a couple of problems with that type of approach is that the first is that most of the hiring managers don’t really understand what data scientists do. You would imagine that if you’re any kind of research organization, if you took a job as a scientist in a lab, you’re gonna work for other scientists or with other scientists. You’re not gonna work for the IT department or the legal department, for example. You’re gonna be around people who understand what you do and can help you become successful. Companies that aren’t prepared to do that, they drop data science talent into their existing structure, usually find that it’s not the right fit for them.
Kevin DeWalt (13m 9s):
They don’t have the right leadership. They don’t have the right surrounding functions, the right systems to make them successful. That’s the biggest initial challenge we see is not having the strategy to get going.
Matt Alder (13m 20s):
How can employers move forward with that? What are the elements that are important in that strategy to be successful?
Kevin DeWalt (13m 30s):
Well, the first one I think is a theme that you’ve probably touched on for a lot of your other interviewees. It’s that I’m always surprised at how companies don’t often do the easy stuff. To give an example, I’ll talk to a department lead at a company and they’ll say, “Hey, you know what, we’re having a really hard time retaining data science talent. Competitor hire away whole team for the last three months and our projects now stalled. What can we do?” One of the first questions I’ll say is, “Okay, do you know why they left?” They’ll say, “Well, it’s because of pay.” Then I would ask, “Okay, so how do you know of it?
Kevin DeWalt (14m 12s):
Did you ask them?” I’m shocked at how sometimes companies just don’t do the easiest stuff. The first recommendation I have is that if you find a candidate who didn’t take a job with you, talk to that person and find out why. Ask people why they’ve left your company. Go to your local events, talk to data scientists, and ask them about whether or not they’d want to come work for your company. Do the easy stuff. Don’t rely on recruiters and human resources to do all that for you and to surface up the macro trends. You have to roll up your sleeves a little bit. That’s the first one. It’s just to do the basics, But beyond that, there’s a couple of structural things they can start doing.
Kevin DeWalt (14m 55s):
Before I go into that, any comments or questions you might have because I imagine that’s a common theme you’ve seen in some of your other interviews?
Matt Alder (15m 3s):
Yeah, I think it is. I think that appreciation for the real motivations that cause people to join, stay at, and leave companies is really interesting. I think it’s something that employers generally really need to focus on moving forward, but particularly at the moment when recruiting remains so difficult and retention is a challenge.
Kevin DeWalt (15m 25s):
Exactly. With that in mind, I’ll give you a couple of quick fixes that we’ve seen work for anyone in your audience who’s looking for something to try to turn things around the next couple of months and then some longer term structural changes. The first thing is to recognize that the data science talent out there really makes one major decision of where they choose to go and why they want to stay at employer is the experience of their hiring manager. When we started seeing the results for this study, we, Prolego, rewrote our job descriptions and led with the hiring manager.
Kevin DeWalt (16m 11s):
As a part of the report, we have an example of this. One of the sections in our job description is meet your hiring manager. We, at the very outset, instead of talking about job responsibilities and background, this is the person who you’re gonna be working for. That sends the message to the candidate about how much we value creating the right environment for where they’re going to work. That is the first tactic. The second one is to actually create a non-management career path for data scientists. At least half of the data scientists we interviewed had a lukewarm feeling about going down a management career path.
Kevin DeWalt (16m 55s):
If you don’t advertise some non-management career path or can’t discuss with candidate about a non-management career path for them where they can stay as a technical expert, you’re, right out of the get go, pushing away about half the people who might be great employees and qualified to work on your company. Those are two nearer term, I guess, tactical actions a company can take. I’ll pause there for a second before talking into some of the structural ones.
Matt Alder (17m 30s):
Please carry on. You can give us a couple of examples of the long term structural stuff. I think that that would be very, very helpful.
Kevin DeWalt (17m 35s):
Longer term, one of the biggest questions we see is, before hiring a data scientist, make sure you actually have data science work. A lot of times, we find that companies will think, “Oh, we need to create a data science program. We have all this data. Let’s hire a data scientist,” but they don’t really have the kind of project work for that person so they quickly relegate the person to do basic creation of user interfaces dashboards or maybe just traditional IT work. The person’s very unsatisfied and they leave. That is very common. The second one is to create a centralized team of data scientists. Along the team of leading with the hiring manager, there was an industry debate a couple of years ago about where to decentralize or centralize your data science talent.
Kevin DeWalt (18m 25s):
Over the past couple of years, the evidence for the better structure is born out based on the companies that are making progress. There’s absolutely no doubt about it, you need to centralize your data science team. Instead of creating a decentralized organization where maybe each individual software project team or product team would hire their own data scientist, unless you’re a very, very, very large company, you wanna primarily have a centralized data science team that can be matrixed out and support multiple different projects. If nothing else, it’s because that’s the environment that the talent wants to work in. If you don’t have that kind of structure, it’s gonna be very, very hard to recruit and retain people.
Kevin DeWalt (19m 7s):
I’ll just end with the third point then on structural. It’s pretty an environment where learning is valued. This is really difficult. A lot of companies, especially when you’re in the pressure of getting the next release out, or you’ve got a backlog work, trying to create an environment where learning is prioritized can be really difficult because there’s a cost associated with that. There are some small steps companies can do, even letting a data scientist spend part of their time, and a lot of these people are so passionate about it. They’ll spend their evenings and weekends working on work if it’s interesting to them, part of their time, where they can explore something new or they can present to a larger part of the organization about what they’re working on and have a structure by which the Wednesday data science lunch session with maybe somebody presents once a month or once every of the month.
Kevin DeWalt (20m 3s):
You can’t be in an environment where learning is prioritized and evangelized, that’s gonna attract the talent of people you need as well.
Matt Alder (20m 9s):
Moving the conversation to recruiting and acquiring the talent in the first place. What advice can you offer to the recruiters and talent acquisition leaders who are listening, who are desperately trying to recruit data science talent to their business at the moment?
Kevin DeWalt (20m 25s):
The first thing I would say is the fastest fix you can make, which you can do in an afternoon, is rewrite your job descriptions to mention who the hiring manager is. Even if it’s just a paragraph or two, or a link to a LinkedIn profile, simply doing that is a very, very quick fix that should instantly raise the quantity and quality of candidates that you get. If you have a non-management career path, advertise it in your job descriptions. Of course, compensation is always part of the discussion here, but I find that most employers are able to figure that out a lot quicker.
Kevin DeWalt (21m 7s):
They can figure out the strategic changes. Then the third one is have an aspirational message. Talk about how the work can impact the company and change the world. There are tens of thousand or perhaps millions of talented people out there who don’t want to go work for a tech company, who don’t wanna work for Google, for lots of different reasons. They don’t want to try to spend their time discovering ways to increase the conversion on clicking on ads by 0.001% over five years. That’s not why they get outta bed in the morning, but if you’re a hospital or a life insurance company, there’s a mission that your company has that helps people and changes the world.
Kevin DeWalt (22m 0s):
You can use that as part of your pitch to people that say, “Look, we’re a life insurance company that’s been around for a hundred years. Sure, you can go to Facebook and optimize a clicks on master ads on people’s Facebook posts, or you can come here and help people find the right type of life insurance policy for them, because when people go through a disaster, we help them get through that.” That will really resonate with people and they will come and work for your company for a lower compensation. Even if you don’t have a lot of other things correct, that aspirational message will attract the people who are likely to become good long term employees.
Matt Alder (22m 43s):
As a final question, as an industry, AI is having a huge effect on us. It’s affecting the type of people that we hire, but also how we hire in terms of the technology that’s coming into recruiting and HR. I just wanna reflect back on the very first thing you said about right at the beginning of the AI rev revolution. I’m just really interested to help give us all some context. What can we expect from AI over the next few years? Where’s all this going?
Kevin DeWalt (23m 15s):
It will completely change how we acquire and recruit talent. We’ve actually worked with some of the largest recruiting companies have been past client far. It household names that your audience would know. If you think about the way the system works today, it is incredibly inefficient. Companies have a need, and to try to fill that need, there are hiring recruiters and posting information on job sites, and at the same time, you have waves of people who might be looking for an opportunity. It’s just happenstance that the two can find each other.
Kevin DeWalt (23m 56s):
If you could take all of the information about your company and where it’s going and your needs and the needs you don’t even know you have yet, you could marry that up with the Infor the information of all the possible people who could be looking for a job, this is an AI problem. AI is going to be able to help us identify the talent we need, help the talent find the place they want to work, and even before we know we have a job, or before someone is looking for a job, identifying the signals that can make that match, and it’s going to drive everything. Within five to 10 years, we’re gonna look back on the current system of recruiting people and just imagine how crazy it is.
Kevin DeWalt (24m 44s):
The idea is that we had recruiters cold calling people, who may or may not be qualified, may or may not be interested, and trying to talk to them about a job that they may not really understand at a company they don’t know that. Well, the current system is so incredibly inefficient and AI is gonna change that simply because the problem is so big and there’s so much data out there that can be leveraged to solve it.
Matt Alder (25m 12s):
Kevin, thank you very much for talking to me.
Kevin DeWalt (25m 14s):
Thank you so, Matt, and I appreciate being on your show.
Matt Alder (25m 17s):
My thanks to Kevin. You can subscribe to this podcast in Apple Podcasts, on Spotify, or via your podcasting app of choice. Please also follow the show on Instagram. You can find us by searching for Recruiting Future. You can search all the past episodes at recruitingfuture.com. On that site, you can also subscribe to the mailing list to get the inside track about everything that’s coming up on the show. Thanks so much for listening. I’ll be back next time and I hope you’ll join me.
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