Matt Evans: Julius Education & the Future of Workforce Tech

Matt Evans is CEO of Julius Education, a company that provides workforce technology tools to help job seekers and employers navigate fast-developing industries. Matt shares his background in emerging fields, including online learning and water technology, and his journey to Julius Education. He highlights the lack of detailed data on occupations, particularly in sectors like clean energy, where job titles are often inconsistently used. The conversation also explores the challenges and opportunities of using technology to provide granular, real-time data for effective workforce planning and engagement. Evans provides examples of successful partnerships and offers advice for building new models. Transcript Julian Alssid: Welcome to Work Forces. I'm Julian Alssid. Kaitlin LeMoine: And I'm Kaitlin LeMoine, and we speak with the innovators who shape the future of work and learning.  Julian Alssid: Together, we unpack the complex elements of workforce and career preparation and offer practical solutions that can be scaled and sustained. Kaitlin LeMoine: Work Forces is supported by Lumina Foundation. Lumina is an independent, private foundation in Indianapolis that is committed to making opportunities for learning beyond high school available to all. Let's dive in. So Julian, it's been fascinating to see how many of our recent conversations on the podcast and in our consulting projects keep coming back to the need for a dual customer approach to bridging talent gaps, one that provides employers and learners or job seekers with the tools they need to navigate fast developing industries.  Julian Alssid: Absolutely, Kaitlin. It's a topic that's becoming more and more critical, especially with a rapid emergence of AI transforming the labor market and emerging sectors like advanced manufacturing, life sciences and clean energy are being particularly impacted, and we're seeing employers in those sectors struggle to find qualified candidates and job seekers often don't know what jobs exist and have the opportunities out there aligned with their own interests.  Kaitlin LeMoine: Absolutely, that's right, and we've been hearing from so many of our guests about the growing mismatch between the skills that employers need and the skills that job seekers possess. This is especially true in fields that are being rapidly transformed by changing technologies and AI where the pace of change is accelerating and the skills gap is widening. Julian Alssid: Yeah, and it's not just about finding people with the right technical skills, but also about fostering those human skills like critical thinking, communication, and problem solving that are essential for success in any field. Those are the skills that will set people apart in an AI driven world. Kaitlin LeMoine: Definitely. And that's why we're so excited to have Matt Evans, CEO and co founder of Julius Education, join us today. Julius education is a workforce technology company that addresses the talent needs of fast moving industries such as energy, semiconductors, advanced manufacturing and others to help them keep pace with the rapidly changing economy using AI and machine learning, the company addresses talent gaps to make sure employers, learners and job seekers have the information they need to navigate these dynamic sectors.  Julian Alssid: Before Julius, Matt was a Senior Vice President at Pearson, where he led the Online Learning Division and served a large network of university partners and adult learners. He's also a co founder of Imagine H2O A leading water Technology Accelerator. So he knows a thing or two about fostering talent in emerging fields.  Kaitlin LeMoine: So without further ado, Matt, welcome to Work Forces. Matt Evans: Thank you. It's great to be here. Thanks so much for having me.  Kaitlin LeMoine: Thank you for joining us. Welcome and we'd love for you to jump in now and to we'd love to hear more about your background and what brought you to your role at Julius Education.  Matt Evans: We are a workforce technology company. We use AI and machine learning to provide really, first of its kind, industry specific labor market intelligence and a suite of workforce tools to support fast moving industries keep up with the pace of change, as you alluded to Kaitlin. And so our work spans the industries that are really dynamic to the economy, from energy and semiconductors, advanced manufacturing, biotech, and we work with a really interesting set of partners from industry such as major employers and industry associations, to regional coalitions, to government agencies at the federal, state and and local level, who are all focused on this work in my background, as Julian you alluded to, has been in the education technology space for the past 20 years, and most Recently at Pearson at the same time, for the past 15 years or so, I've been on the board of this organization driving water technology innovation called Imagine H2O and what that experience really drove home for me was for all these critical industries, Water among them, but not exclusively. We need new technologies, we need new policies, we need new forms of capital for these industries to thrive. But there's this whole people component, which historically has not been getting the same level of attention, and so that's really was the the innovating spirit behind Julius particularly in these industries, which we alluded to Kaitlin, which are going through significant change, with new jobs coming on board, new skills required, new employers popping up, and so that really is the context for for the work that we do at. That Julius,  Julian Alssid: Yeah, well, it's certainly, it's certainly timely, Matt. I guess I'd love to hear, we'd love to hear some more about the problems that Julius is trying to address. You know, kind of digging a little bit further.  Matt Evans: The core problem that our partners are facing is they simply don't have the data on the occupations that they care most about. So when they're trying to answer questions like, What is the demand in my region, in my specific industry, for specific roles down the job title level, if they're trying to understand, what are the skills and credentials that employers are actually looking for. If they're trying to say what is the future going to look like across all these dimensions, they just don't have the data that they need to understand that. And so with our approach, using AI machine learning, we're able to ingest large unstructured data sets and give them a real time view in the way that their industry talks about these jobs for which data previously, there had not been any data currently available for it. So that's the kind of the core the core problem. I think the change context that Kaitlin was teeing up is certainly exacerbating a lot of this dynamic so with the ripples of technology through the industries that we've touched on that's creating real complexity for folks who are in key decision making. Seats to say, how do I align an ecosystem or my resources to drive workforce outcomes? How do I understand exactly what employers need today and also going forward for the jobs that are important to them, for other stakeholders, like learners and job seekers, they're trying to understand what is this field? What are these job titles? What are the career paths when I go to indeed.com What am I supposed to be putting in here? What are skills do I have today, and how do they transfer into this potentially exciting new industry for me? So those are some of the kind of core problems at the stakeholder level that we are, that we are seeing folks had historically been trying to get at this. This is not a new problem, even though it's been exacerbated now by accelerants and change in technology. You've been trying to get at this with legacy tools. So doing things like employer surveys is one approach to get data on the local labor market needs that is often challenging, just given response rates and also given latency of getting the data back. And these things, entries are moving so quickly that often that data is stale by the time the surveys come back. If it's kind of 12 or 18 month cycle, other approaches have been to say, hey, can we use legacy data sets that oftentimes are some version or tied somehow to Bureau of Labor Statistics, SOC data. That's a little wonky, but that that that sock code folks may be aware of on this podcast, but that's also challenging, because those are really economy wide views, and they don't work for the industries in which we are, which we're focused on, or, you know, third folks may have great data for their industry down five jobs deep, but what they're really need is something that goes 250 jobs deep for their particular industry, within energy or within semiconductors, advanced manufacturing, biotech, etc. So really, that core issue is where Julius comes in, and we are supporting our partners really, with this very detailed, industry specific depth of data, and so we just take a fundamentally different approach to to provide that for, again, for these occupations that the data just doesn't hasn't previously existed. Kaitlin LeMoine: What is your process for beginning to unearth that level of detailed data? Matt, as you said, right, there are so many legacy tools, but what does it look like to live in this space of kind of rapid change and transformation, and how do you go about remaining current in that. Matt Evans: It's almost setting up the data infrastructure is kind of the way we think about it. Because, as you're alluding to, it doesn't really do folks any good to have kind of a, just a one time snapshot about what's going on across these, these, these jobs or labor market data that they care about. It's really how does their repeatable and ongoing view into these, these data needs. And so that's where our where our own kind of proprietary data and AI classification models provide for that ongoing data infrastructure which provides a current view on an ongoing basis. So that really is kind of the foundational piece of it, which is it gives us that ongoing pulse and allows us to continue to keep up with the pace of change with these industries, not just for today, but going forward as well. Great. Kaitlin LeMoine: So I mean. Diving a little bit deeper, please tell us. What are some current projects that you're working on, either within clean energy or any other sector you'd like to discuss. And what are you learning? Matt Evans: In clean energy? There's, there's a ton of things to point to. Maybe I'll call out two or or three. One example at the state level, one of our wonderful partners is Mass CEC, which is the Massachusetts Clean Energy Center, really leading the clean energy initiatives for for Massachusetts, they they're very focused on the workforce, in the workforce issues of the Commonwealth, and they've been through all the legacy tools that I was alluding to, the challenges in many of these kind of important sectors. In the clean energy economy, there's real fragmentation with how employers are talking about different roles. So they may employers may be using the same job title for for the same different job, excuse me, for different, for the same, for the same role, which is causing a lot of the lot of the complexity. So for Massachusetts, there's a priority set of occupations that they're trying to get much more precise on what's going on in labor market, one of which, for instance, is a role called an energy auditor, which is an essential role for energy efficiency and for high performance buildings, because they're the ones really helping to diagnose where there are energy inefficiencies and efficiencies within a within a minute building. It's also a critical role, because in some ways it's not just kind of the output of a lot of effort focused on energy efficiency. In some ways it's the upfront constraint to ensure this actually happening. It's a leading indicator to say, hey, if we have enough energy auditors or not, because if we don't, there's no way we can effectuate the rest of the energy efficiency strategy we need for for buildings. So it's a really, really key, key role. The challenge that we found in Massachusetts is that job is being called 40 different things by employers throughout the state that is, makes it so hard to get accurate understanding about what is employer demand for these roles, where in what county, skills certifications, all the kind of knock on on questions. So that would be kind of one really interesting example. There are other roles in that same kind of world, with with with mass CEC, like job titles, like assembler, which is can be a really important role in everything from electrical assembler, mechanical assembler, working assembling on kind of solar solar farms, but that catch all title or assembler really makes it kind of challenging to understand. Are we talking about junior level assemblers? Senior level assemblers? What kind of what segment of the clean energy economy actually talking to so you're trying to align the Massachusetts ecosystem with programming with dollars to serve employer needs. Getting much more precise about what we mean by these rules is really, really essential, and our data reveals that. So that so that would be another example of the work in Massachusetts as a learning. Julian Alssid: I can totally get the titles and the, you know, like and AI being really helpful and beginning to see through the different titles and find some common data grounding. Cutting across multiple industries, can you speak to other learnings that you that are transferable across industries.  Matt Evans: One other, I think, example that that is, I think we found really interesting is we do a lot of work with employers, utilities in particular, and they're really interested in looking around a corner to understand the skills of the future and how their workforce is going to be impacted by macro trends in the energy landscape. So we've done some really interesting work with a major industry association called EPRI, the Electric Power Research Institute, to develop a framework which helps lay out how the mega trends shaping the industry impact job cluster areas as well as impact down to specific occupational areas. And so, for instance, if you're looking at, say, the impact of EV adoption, well one obvious place where that's going to show up is in kind of field technicians or EV kind of focused technician roles, but it's also going to show up in other jobs which don't necessarily have EV in the job title. So skills, what is a someone who's running programs and utility in which EVs is a part? What kind of skills do they have related to EVs? What does a customer service rep need to know about EVs, as well as when they get when they get questions about that. So really thinking kind of beyond specific roles, but for the skills impact, and that kind of framework is certainly applicable to other industries, about how do you think about what those mega trends are, and then how those cascade down to specific roles? And then, obviously with the data infrastructure. You're seeing, tracking how that really is being expressed by employees, and are they closing the gap between what needed for the skills of the future by role and then? And actually, is it is actually happening?  Kaitlin LeMoine: Yeah, I think that's a really interesting challenge. I mean, I guess it has been over the years, but I feel like it stands out more so than ever before, really, that, you know, there, it's like there are all these skills that you fall, that fall into many industries, and do employers recognize that? And do learners know and employees know how to talk about that? And is there that common understanding that, oh, well, the skills I'm using in this industry also apply in this one maybe with, like, this one little area of upskilling, right? Or something like, it feels like there's a lot of room for that, and room for building understanding and clarity around that transferability. Matt Evans: And also for learners and job seekers as well. So as I think, another kind of transferable application as well, to kind of bridge both your questions. We did some work with the DOE analyzing advanced manufacturing, the intersection advanced manufacturing, semiconductor roles, and how we can bring that to life in the right kind of career navigation solutions for understand, for folks to understand the progression pathways. So we looked at 100 different roles within the sector, and 250 different career pathways to really help people understand, what are these jobs, which I may have heard about, maybe I haven't really, what are the career pathways? What does that look like over time? And interestingly enough, over 50% of the jobs and did not require a four year, four year degree. And so that story also is, I think, an important one across many of the dynamic industries in which we work, certainly there are roles which require kind of higher level degrees, for sure, but there's also a lot of career pathways which which don't and so bringing that to life for learners and job seekers, I think, is really important aspect that certainly is transferable across a lot of these a lot of these sectors.  Julian Alssid: I'm interested that, looking at your website, and given your sort of intro, you're, you know, you're sort of similar to our consulting work, you know, sort of workforce development being this kind of, this intersection of work and learning. Be being multidisciplinary. It sounds like your kind of partnerships and partners and are being used by many different types of groups, from government intermediaries to, you know, employers and I'm assuming, educators and not individuals. Where are you seeing sort of the greatest pickup at this stage in terms of groups that are really looking to get on board with and bake this kind of data use into their work.  Matt Evans: The folks that are, I think, particularly focused on kind of the data and consuming that data infrastructure are folks that we think are almost these kind of conveners. So they're really convening stakeholders, and they need the data to really kind of align the ecosystem around a commonly understood set of employer problems. So that convener hat we've seen is is worn by some industry association. It could be worn by a utility because oftentimes they're thinking actually more broadly than a typical employer is about their region, but certainly kind of regional, kind of coalitions are important part of of that. And then there are certain kind of government agencies who are also getting that charge, such as such as Mass Mass EC. So the kind of the the intersection really is these fast moving industries and those kind of convener types, but they may wear kind of different, different hat, depending on the particular region, the protection of the industry in which we're which we're talking but that's, that's the common thread. Julian Alssid: And just Just a follow up to that. I mean, we've seen over the years, often data tools, you know, are purchased or used by organizations, and they kind of end up dying on the vine or shelves or whatever, the digital equivalent of that would be collecting dust somewhere. How do you ensure that that doesn't happen with your client, your clients and partners?  Matt Evans: It's a it's a great question. There is often, we've seen this the same the same thing, and so there's often a conversation that we continue to have with our partners on an ongoing basis and with the ecosystem of partners that they're bringing into it that needs to be had about how do we make sense of this data, and kind of, what do we do about it? I think what we've seen is the data provides not only kind of a roadmap of what's needed, but it's an accelerant of aligning the stakeholders around a common set of understood problems. So if we're trying to, say, engage employers effectively, or make sure we're reflective employers needs for a particular industry in a specific region. So it's we found it so much faster to have the data or workforce tools in which they're at the table, and we're talking about that, versus kind of people just staring at each other, kind of starting, starting from from scratch. So that really is a kind of a great kind of use case about how this really kind of plugs in. It plugs into people's kind of changing for driving workforce outcomes. Kaitlin LeMoine: Building upon that last point, Matt, you know, I mean, we, we always ask some version of the question I'm about to ask on this show, given that the show is workforces. But from your perspective, you know, what are some practical steps that our audience can take to become forces in using these types of tools effectively, on in their own initiatives, in their own work, especially in the case of fast moving industries, but maybe just in general. I mean, please take the question in whatever direction you'd like. Matt Evans: Really, it's, I think it starts with a question which is really centered around, does the existing data that I have give me the insights that I need to understand some of the questions that we've been talking about. Do I really understand the specific needs of employers with down to the role level? Do I really understand the skills that are attached to those roles? Do I really have a good understanding of what the future looks like for for forecasting or for for skills that often, I think, is the simplest, but are super powerful place to for folks to to start. And I think in some instance, the answer may be, Yep, I got what I need. For many of our partners, they're saying, well, actually, I don't I the data that I, that I looking for, does not exist. I really need these industry specific, specific solutions give me give one example in another industry, in biotech, one of our partners was kind of asking themselves the same question as they were trying to forecast the biotech workforce in their particular region. They've been using legacy data sets, including stuff at SOC Code or BLS level data. And it turns out, they were dramatically under because that is hiding in so many ways, or it doesn't even capture the full range of occupations that are needed in the industry. They were dramatically under forecasting, actually the workforce that they need in the region over the next, kind of seven to 10 to 10 years. So really, just starting with that simple question led them to kind of that insight. And I was maybe kind of a powerful place for anybody to start. Julian Alssid: Yeah, that is powerful. And it's good to know that we're moving forward with more granular, actionable data, because I think that so often we've been using, you know, a sledgehammer when we need a scalpel. Matt Evans: Yeah, we often, the analogy we often use is, you know, go from using glasses to actually using a microscope, so you really get the level of specificity that you need down to that job level, again, for roles which were not previously covered. Julian Alssid: Well, this has been really, really fascinating, Matt, and I'm sure will be of interest to many in our audience. And so how can our listeners learn more and continue to follow your work and Julius Education? Matt Evans: Well, obviously we'd love to connect with anyone on LinkedIn. Our website is another place to engage or for folks to reach out. Would certainly love to love to pick up any threads will be helpful. Our website is juliusedu.com we're also frequently at many of the conferences and workforce conferences that folks will be attending, I'm sure, who are listeners. So whether it's ASU, GSV, or Horizons, would look forward to the opportunity to say hello in person to folks. Too. Kaitlin LeMoine: Great. Well. Thank you so much for taking the time to speak with us today, Matt, we've learned a great deal from you, and look forward to continuing to follow and track your progress and exciting work ahead of you.  Matt Evans: Thanks so much for having me.  Julian Alssid: Thank you, Matt. Kaitlin LeMoine: That's all we have for you today. Thank you for listening to Work Forces. We hope that you take away nuggets that you can use in your own work. Thank you to our sponsor, Lumina Foundation. We're also grateful to our wonderful producer, Dustin Ramsdell. You can listen to future episodes at workforces dot info or on Apple, Amazon and Spotify. Please Subscribe, Like and share the podcast with your colleagues and friends.