Solutions Spotlight on AspenTechLabs

Chris Hoyt: All right, everybody. Welcome to the Recruiting Community Podcast. I’m Chris Hoyt, president of CXR, and I’ll be your host today. We’re doing one of our special editions — what we call a spotlight — and I’m super excited about this. These are episodes where we shine a light on interesting work being done in our space.
Today I want to talk about Aspen Tech Labs. They’ve been a CXR community member for years, and they’re excited to start sharing their newest product — what they’re calling a next-generation business intelligence tool for the TA employer industry. It covers what TA leaders and practitioners need to understand about the job market for each opening: supply and demand impacts, competition, wage and salary analysis, labor market availability, and a lot more.
We’ve got Tim here from the Aspen Tech team to walk us through what they’ve been working on. A couple of quick things before we dive in: we’re streaming on YouTube, Facebook, and LinkedIn, but you can check out all of our past episodes — hundreds of interviews with TA leaders, practitioners, and guests like Tim — at cxr.works/podcast. You can also find an easy way to like, subscribe, and make us internet famous. If you’ve got someone you think would be a great guest or is doing good work you want us to spotlight, let us know. And as a last reminder, we are an ad-free labor of love. Nobody paid to be on here. With that, let’s get started.

Announcer: Welcome to the Recruiting Community Podcast, the go-to channel for talent acquisition leaders and practitioners. This show is brought to you by CXR, a trusted community of thousands connecting the best minds in the industry to explore topics like attracting, engaging, and retaining top talent. Hosted by Chris Hoyt and Jerry Crispin, we’re thrilled to have you join the conversation.

Chris Hoyt: Welcome, Tim. How are you?
Tim Dineen: Thanks for having me. I appreciate it. Looking forward to chatting.
Chris Hoyt: We haven’t had you on the show before, so we appreciate you jumping in. We didn’t do a whole lot of prep for this, so it’ll be a lot of fun. Not quite a hot seat — maybe a warm seat. Before we dive in, Tim, give us a little escalator pitch. Who is Tim, and why should we be paying attention to him today?
Tim Dineen: I recently joined Aspen Tech Labs — about nine months ago at this point. My career started in web development and internet marketing consulting, but I joined Indeed very early on, almost 20 years ago. I was an early employee there and served as their sole consumer marketing person at the outset, focused on SEO and SEM.
After two and a half years, I felt the job was done and left to co-found Recruitics. Recruitics is known for bringing programmatic advertising to our space for the first time, and it evolved into an agency with quite a number of first-to-market products. After 16 years there, it was time for something new, so I joined Aspen and now have a lot of additional data that I’m looking to turn into products.
Chris Hoyt: I love a bright and shiny penny on the show. Let me ask you — before we jump in — Aspen has essentially this 17-plus year history of being a data layer that sits underneath job boards and TA platforms, and this new Job Market Pulse puts you on the front end, directly in front of the employer. Was there something the team kept seeing in the data that made you decide to build this dashboard yourselves rather than selling the feed to someone else?
Tim Dineen: The company has been around about 18 or 19 years and is well-known as a scraping business — scraping jobs from direct employers for job boards, ATSs, and anyone else in the TA industry who needed that data. Selling access to that data has been happening for years as well. When I joined, there was a business intelligence platform already under development with a few people in the industry using it. I took a fresh look and felt like we could do more — make it available to more people, make it easier to access, and add to it. We have additional data sets that didn’t exist before. But really, it’s about making it accessible and tailored not just for job board folks, but for people who are trying to hire every day.
Chris Hoyt: I love that. And you have an ask at the end of this for folks who are tuned in, so we don’t want to lose track of that. Before we do the screen share, can you walk us through the supply side? The organization’s heritage is scraping employer postings — that’s demand. But the platform is claiming to lead with true job seeker activity and real supply-demand ratios. Where does the actual job seeker signal come from, and how are you measuring it?
Tim Dineen: There’s a proprietary aspect to where we get the people data, but I’ll share what I can. We’ve had demand data for years — historically at least a couple years of storage, with scraping going back even further. On the people side, we also brought in a lot of economic and government data. But for job seekers specifically, we’re not just scraping LinkedIn like everybody else or trying to become a sourcing platform. We found a proprietary data set with clear indicators that a person is an active job seeker. It’s about 100 million records of real people in the United States — who they are, when they searched for a job, the type of job they’re looking for, their geographic region, where they’ve worked. So we wanted to go beyond a demand-only platform. From my past work at Recruitics and working with employers, I know both sides are extremely important.
Chris Hoyt: So before we jump into the screen share — and you mentioned this yourself — you’re entering a room with no shortage of competition. For a TA leader who might already be paying for a similar service, what is the specific thing JMP does that others can’t? And maybe what is JMP deliberately trying not to do?
Tim Dineen: The differentiator for us starts with data quality. We’re not scraping LinkedIn or job boards, which can introduce spam, duplicates, and noise. We’ve got years of clean data from scraping directly from company career sites. We’re up to 300,000 companies scraped every day and pulling in 11 million jobs daily — active jobs, clean data, deduplicated. On the people side, same story: no duplicates, no guessing about where someone is or what they might be doing, no government survey data that’s been sampled and extrapolated. We’re talking real numbers.
Beyond data quality, we’re focused on being easy to use, affordable, and flexible. We’re building this from scratch and looking for people to help really shape what the product becomes. We think there’s room for a better, faster, more straightforward product at a price that makes sense.
Chris Hoyt: I love it. Well, show it to us. Let’s see it.
Tim Dineen: So what we’ve pulled together is supply data — the people — and job data, which is the demand. We’ve also extracted signals like salary and wage data, which is fresh, recent, and real — coming directly from companies that are actively hiring, not AI estimates or survey data. We’ve got visibility tools and competitive intelligence as well.
I’ll note I’m demoing this off my laptop as we work on productionizing the platform, so some things may look a little different in the final version. But we have a number of modules to start with, so let me dive right in.
You’ll see we have a few different views — light and dark background options, a simple view and a deeper data drill-down view. We’ve got three main sections. The first is a labor market overview where you can drill down into a specific area. The job seeker data is shown in light blue, economic indicators from the government in orange, and our job postings from Aspen Tech Labs in purple.
Every graph is drillable. If I select a state — let’s say New York — you can see there are 132,000 active job seekers, see the number of job postings in that area, and get a supply-demand ratio. In this case, supply does not meet demand. You can drill into subcategories and roles. If I click on Accountant, the graphs refresh instantly. This lets you understand the market: Is the supply there? Am I promoting in the wrong geography? How competitive is it?
The next view shows active job seekers in more detail — where they are, expected salaries, and employment trends in that area — all drillable by category and geography over time.
Then there’s the job trends view. I’ll select Healthcare, which we all know is performing fairly well. It shows what’s currently available and some background on the industry.
We’ve also got a Job Explorer tool where you can quickly see who the top employers are in a given area, for a certain job type, or within an industry, and see their specific postings.
To tie it together, let me jump to the My Jobs section. I have a couple of companies preloaded as samples. This is the section where we think most employers will start. Aspen is likely already scraping jobs from a given company, so when that company logs in, they see a view of their own postings — what’s coming out of their ATS, what’s being promoted, how many jobs they have, organized by category and geography. They can see which jobs have salary data associated and what job seekers will know about those positions.
Another view focuses specifically on salary. In many cases we can spotlight whether a company is out of compliance with local salary disclosure requirements, or simply whether they’re paying at market rate. I happened to click on a role that had a posted salary, and what it shows is Aspen’s market rate based on years of data — from the 25th percentile up to the 75th or 90th, plus the median. In this case it’s for a surgical tech in that specific region, right now.
Chris Hoyt: Let me ask you about that, because I think it’s really interesting. We’re talking salary benchmarking here. You’re pulling posted ranges, and we do have pay transparency laws — but not everyone is fully complying yet. And those ranges have become super wide, sometimes pretty performative. How close is a posted range to what someone might actually get offered?
Tim Dineen: We have our own ranges based on what other companies are actually paying within that 25th to 75th percentile band. So if your range is outside of that — or you’re going from zero to a hundred — you’re probably going to end up paying somewhere in what we’re projecting anyway.
Let me pull up a data scientist role. We believe the median right now is $137K. If you’re posting outside even the 90th percentile, there are a few ways to look at that: you’re probably going to attract strong applicants, and if you’re actually paying that rate, great news for the candidate. But we see a lot of the opposite — jobs where companies are underpaying or attempting to underpay, and that’s often why they’re not getting candidates, or not getting the right ones. We’re really just helping you understand whether you’re paying the right amount, and what impact that has on your pipeline volume and quality.
Chris Hoyt: I think that’s really helpful benchmarking for TA leaders who are posting accurate comp ranges and struggling to compete with some of those wide-variance posts from others. I also want to talk quickly about ghost jobs and evergreen reqs. We know there’s legislation coming around whether jobs tied to a posting are real, or whether they’re just stale or perpetual posts. If you’re tracking competitor hiring velocity from all these scraped postings, how is Aspen Tech separating a company that’s actually ramping up from one that’s just leaving stale reqs out there? Is there a confidence level — like, Company X is hiring 40 engineers vs. Company X just never took their req down?
Tim Dineen: One of the nice things about our data is that a lot of the ghost job problem happens on job boards, where people repost to rank higher or get fresh eyeballs. We’re not looking at job board data. We also exclude staffing company data, which is where a lot of duplication and evergreen posts occur. So we feel the data is pretty clean to begin with.
That said, yes, some companies do leave jobs up indefinitely. We track the date a job was posted, how long it’s been live — what we call job age — and that’s a useful signal. How quickly a job gets filled can also indicate velocity. We know when a job goes stale. We sometimes call them zombie jobs — if a company has two career sites, one of them might just be old.
Chris Hoyt: That’s a really good point, and worth doubling down on: removing staffing organization postings. I think those are largely the target of the proposed legislation and guidelines around ghost jobs. Reminding people that gets pulled from your data is worth highlighting.
Tim Dineen: Agreed. We start with clean data, and if anything needs to be cleaned up further, we will. I also want to show you what we’re calling the Job Market Research page, which I think will be highly useful for direct employers. This is more of a research view — if you’re preparing to launch a new position, you can quickly see the supply and demand before you even post. It shows posting volume, wage benchmarks, employment trends, and competition all on one screen.
For example, if I randomly click on Transportation in Connecticut, it shows supply is heavy there — so you should have a fairly easy time filling that role. In a different job type or market, it might be much harder. I’ve already worked with a few companies looking at this data together, and it’s helped them understand why they’re struggling to hire: high competition, low available candidates. Now they have the data to explain it and make better decisions.
Chris Hoyt: Tim, I can’t let you off the hook without talking about AI. There’s an AI risk and opportunity score component to this. Can you walk us through that and how confident you are in it as it gets built out?
Tim Dineen: Sure. Two things. First, I’m a fan of AI and I’m very aware of its economic impact. In terms of the product, we are incorporating an AI component — if it’s easier to just ask a question like “Am I paying enough?” you can chat with that interface directly.
But what you’re referring to is a feature where we incorporate data from Anthropic to indicate whether a role is likely to be replaced by AI, and to what degree. And we’re looking at it from multiple angles. It’s AI risk from the perspective of someone who does that job — but it’s also potentially an opportunity for an employer. If a role has high AI exposure, that might be a signal to combine it with another role and realize some savings. There’s a lot of new data coming out around AI risk and exposure, and we’re going to incorporate that in a way that’s genuinely useful for employers.
Chris Hoyt: Where that gets interesting to me is it starts to push this into workforce planning territory. We’ve seen some of the earlier supply-demand data used for decisions like “Should I drop a call center in this city?” But now you’re adding a layer that tells someone whether to bet on a workforce planning decision given what’s happening with AI. I love that you’ve pulled in the Anthropic data, and I know there’s more to come as you lean into it. This piece almost changes what the tool fundamentally is — at least from a TA leader’s perspective.
Tim Dineen: Yeah, and I apologize for jumping around a bit. But what we really hope is that every signal we get — every job, every candidate data set — we can translate into something actionable. Graphs and charts are nice, but what do you actually take away? Is this a hard-to-fill job? Should I spend on the big job boards, or should I focus on sourcing, staffing, events, something else? We can start to surface those takeaways from each signal, and that includes AI exposure and compliance. Things like: this role is high AI risk; this job is not in compliance with local salary laws. Right now it shows up as a small indicator on some charts, but we plan to expand that into real alerts — your job is not in compliance, you’re essentially not following the requirement, here’s what you need to address.
Chris Hoyt: Who is the ideal buyer here — the head of TA, or are you starting to see senior HR and workforce planning folks coming in too?
Tim Dineen: I think both. Through most of my career I’ve worked with TA professionals and direct employers working with agencies, and I’ve heard from a lot of them that they don’t have access to this kind of data — maybe an agency pulls something together and it ends up in a PDF four months later, or a job board shares some data on their own terms. A lot of folks would love to have this at their fingertips. Workforce planning is a great use case too. I’ve been involved with plenty of situations where you’re trying to promote a job and the candidates just aren’t there, or there’s a geographic mismatch between where the job is and where the talent is. If you do that planning before you post and really understand your supply-demand picture, that’s a huge use case for this data.
Chris Hoyt: What’s the pricing model? Licenses? Seats? All you can eat?
Tim Dineen: We’re not announcing exact pricing just yet, but we’re keeping it simple. There will be a free version with macro-level data accessible to everyone. Right now we’re looking for participants in a kind of product council to help us determine pricing tiers and get feedback. We’re offering free access for the year while we figure out what’s missing and whether there’s anything custom we need to build for certain employers.
Ultimately, we’ll have a standard monthly rate that we believe will be better than what the competition charges. The free tier will be macro-level. The middle tier will let you drill down into metro areas and specific job categories. The enterprise level will include the detail you saw in My Jobs and My Competitors.
Chris Hoyt: You can’t just drop “we’re looking for people to try this out for free” and leave it there. So who’s the right person? Do they need to be a senior recruiter, director, head of TA — who should reach out?
Tim Dineen: I’d say any employer with a TA team — so not a single recruiter at one desk, but a team that likely has 100 or more open reqs at any given time. And every team is different — who are the data people? Who likes to dig into numbers? Who pulls reports for others? I don’t want to be too prescriptive about a specific title. Even if you’re already using one of our competitors, we’d love for you to take a look and tell us if we’re on the right track. Take a look at our data, see what’s different. We believe it’s better data, and we hope the product is ultimately highly useful.
Chris Hoyt: All right. So Tim, you’re representing Job Market Pulse. Anyone interested can find him on LinkedIn, or reach out to us and we’ll make a direct introduction. I’ll take this to our community forums as well — we have some real data junkies in our membership who love to get their fingerprints on something like this, so it’ll be a great fit. Tim, anything else before we let you go?
Tim Dineen: No, just what you said — I’m open to anyone who wants to reach out on LinkedIn. Let’s chat. We’ll jump on a call and look at the data together. I’m looking forward to the conversations.
Chris Hoyt: Good luck with this, and thanks for joining us, Tim. We appreciate your time.
Tim Dineen: I appreciate it. Good to talk to you.
Chris Hoyt: You got it. All right, everybody — until next time, cxr.works/podcast. You can also check out the Aspen Tech Labs profile on the CXR Marketplace at cxr.works/marketplace. We’ll drop this video there too, so if anyone needs a quick recap, it’ll be right there. We’ll see everybody next time.

Announcer: Thanks for listening to the Recruiting Community Podcast, where talent acquisition leaders connect, learn, and grow together. Be sure to visit cxr.works/podcast to explore past episodes, see what’s coming up next, and find out how you can join the conversation. Whether you’ve got insights to share or want to be a guest on the show, we’d love to hear from you. If you’re interested in learning more about becoming a member of the CXR community, visit us at www.cxr.works. We’ll catch you in the next episode.