10 Dec 2025 · 45m · Daniel Whitenack, Chris Benson, Ramin Mohammadi · 21 quoted facts
The episode argues that entry-level AI and data science jobs have effectively become mid-level jobs. Guest Ramin Mohammadi says generative AI now does the routine tasks juniors once learned on, and employers want people who can already build, deploy and maintain real systems, not just explain theory.
The central tension is between universities and industry. Curricula change slowly and focus on theory, while industry moves fast. Ramin describes a hands-on MLOps course as one fix, but warns that learning by building costs money, that AI use can erode skills, and that industry should help academia.
Routine tasks like writing SQL queries and building dashboards used to train new hires. Ramin says AI now does these, so he stopped hiring many interns and companies prefer a few proven people over many trainees.
Ramin says industry moved from 'good accuracy in a notebook' to owning the whole pipeline, including containers, automated deployment, monitoring and drift checks. Theory and Python are now just the ticket to the game.
Ramin says approving a new course takes so long that tools change several times in the meantime. He cites an MIT study saying most AI PhDs go straight to industry and nearly all leading systems come from industry labs.
Ramin's Northeastern MLOps course has student teams build a real product over a semester and demo it at an industry expo. This gives them the build-and-deploy experience hiring managers say new graduates lack.
Ramin's survey found many students take extra online courses and join hackathons, but practicing with cloud services costs money. Students already paying tuition struggle to afford it, so he asks companies like Google for cloud credits.
Ramin Mohammadi says AI now handles the routine tasks that juniors used to do, so companies hire fewer trainees. Entry-level postings often ask for experience and the ability to build and deploy real systems.
MLOps means the engineering work of putting machine learning models into real use and keeping them working. Ramin says this includes packaging software in containers, automating deployment and watching for drift, which is when a model's performance slips as real-world data changes.
Ramin says yes, because the theory is non-negotiable, but it is no longer enough on its own. He advises building a portfolio of real projects, since employers want proof you can ship working systems.
Not fully, according to Daniel Whitenack. He used AI to write most of the code for e-commerce forecasting, but still had to choose the right modeling methods and understand the trade-offs himself.
Ramin suggests learning how AI works in general and building simple projects through workshops, without needing the underlying math. He says many AI systems can be built by putting components together.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Ramin Mohammadi says Harvard Business Review called data science the sexiest job of the 21st century in 2012, which sparked a gold rush of university programs.
“it was about like a decade, decade ago, back in 2012, uh, Harvard Business Review, they called data science is the sexiest job of 21st century.”
Ramin Mohammadi says universities rapidly launched master's programs after the data science hype, promising employability after learning some machine learning.
“Universities were spinning up the new master programs overnight, and the promise was pretty simple get a degree and learn a little bit of machine learning, and you're set, you're instantly employable.”
Ramin Mohammadi says entry-level AI job postings often require about three years of experience.
“We we see job posting for entry level, you know, that that job requires about three years of experience.”
Ramin Mohammadi says Google Cloud's Rules of MLOps laid out that successful machine learning needs real engineering practices such as containerization, CI/CD automation and monitoring.
“Some resources like, uh, Google Cloud, Rules of ML Ops, you know, it. It re laid out these new realities that successful ML needs a whole suit of real engineering escapes.”
Ramin Mohammadi says the generative AI explosion around 2023 is visible in the Stanford AI Index and acted as an automation event hitting entry-level work.
“And that becomes like, around 2023 explosion that you can see in the A Stanford AI Index.”
Ramin Mohammadi cites a study from OpenAI and the University of Pennsylvania on task exposure to large language models, concluding repeatable junior tasks are highly vulnerable to automation.
“there was this study, uh, from OpenAI and University of and, uh, of Pennsylvan, Pennsylvania, uh, that they look at this task exposure to large language model”
Ramin Mohammadi says he used to hire many interns but hires fewer since the AI shift, because AI now handles those tasks.
“For example, I used to hire lots of interns, uh, to basically help with the development and speed up the process. And since AI shift, to be honest, I just don't use AI for all of those tasks”
Ramin Mohammadi paraphrases a recent post by Andrew Ng arguing education must shift toward practical skills, saying knowledge is great but skills are greater.
“there was this new post, uh, by Andrew Ng recently that he argued, Um, this urgent shift in the education, and I'm going to paraphrasing what he said. He said, knowledge is great, but skills are greater.”
Ramin Mohammadi cites a recent MIT study saying about 70 percent of AI PhDs skip academia and go directly to industry.
“Basically, they say that the uh. Right now, about 70 of the AI PhDs are just skipping academia and go to job market, go to basically industry directly, and that's a huge brain drain for the universities”
Ramin Mohammadi cites a statistic that 96 percent of major state-of-the-art AI systems come from industry labs rather than universities.
“Like, 96 of the major state of arts systems comes from industry labs, not from universities anymore.”
Daniel Whitenack says Purdue University's new building, completed around 2024 as the Hall of Data Science, was renamed the Hall of Data Science and AI.
“They did rename it Hall of Data Science and AI, so they, to their credit, they, they caught, uh, they at least caught up with the name.”
Ramin Mohammadi says his Northeastern MLOps class has roughly 150 to 270 students per semester.
“every semester, right now we have about 150, 270 students within one class, like a huge classroom.”
In Ramin Mohammadi's course, student teams spend a full semester building one real product in a domain they choose, such as healthcare, finance, sport or robotics.
“Then, as a team, they spend the entire semester on building one real product, and this real product. It's not just homework assignment, it's not a toy example”
Ramin Mohammadi says the course's MLOps Expo was partnered with Google and scheduled for December 12 at Google's campus in Boston.
“this year, for example, uh, we partnered with Google, uh, so we are hosting on, um, in two weeks, December 12th, at Google Main, Uh campus in Boston”
In Ramin Mohammadi's survey, about 60 percent of his students said they take online courses on top of their schooling.
“which is 60, basically of the of the students. They said that they are taking online courses on top of what they are taking in in the school.”
In Ramin Mohammadi's survey, about 82 percent of students said they take part in hackathons to learn to build quickly.
“another question 82 of the students say that they are participating in hackathons in order to learn to how to quickly to build.”
Ramin Mohammadi says Google provides cloud credits for his MLOps course every semester so students can build real systems.
“they give us lots of credits for our ML Ops course every semester. Because, uh, our students, they can't otherwise build anything in a real world.”
Ramin Mohammadi says Northeastern's Khoury College of Computer Science is updating its curriculum in 2026 with more practical courses and ethics woven into coding.
“Northeastern, Khoury College of Computer Science. Um, the as of this year, basically, 2026, they're updating their curriculums finally”
Chris Benson says he sponsors capstone projects at Georgia Tech's College of Computing through his nonprofit role.
“I'm a capstone sponsor for capstone projects at, uh, at Georgia Tech, uh, in the in the College of Computing”
Daniel Whitenack says he helped his wife's e-commerce business with customer lifetime modeling and 2026 forecasts during Black Friday and Cyber Monday, using AI to write much of the code.
“my wife owns a e commerce business. Black Friday Cyber Monday just happened.”
Ramin Mohammadi says he bought a Reachy Mini desktop robot, expected to arrive in early January, and plans to build apps in a simulator beforehand.
“Recently purchased the Reachy Mini by Arm.”
Ramin traces the field from the 2012 'sexiest job' label to a market where entry-level roles demand years of experience and real deployment skills.
“we see job posting for entry level, you know, that that job requires about three years of experience.”
Engineering practices like deployment and monitoring, then the 2023 generative AI wave, changed what employers expect from data scientists.
“You need to own the pipeline.”
Repeatable junior tasks are automated, so companies hire fewer people with proven skills instead of training large cohorts.
“they rather to hire five or maybe ten people that already have built or developed some complete system from day one.”
Curricula stress theory and move slowly while industry labs set the frontier, leaving graduates unprepared for deployment work.
“they know exactly how the engine works, but they actually never tried to drive a car in the traffic.”
Ramin describes his Northeastern course where student teams ship a real product and demo it at an industry expo.
“they start by choosing a domain that they actually care about healthcare, finance, sport, robotic and whatnot.”
Daniel shows AI still needs a skilled orchestrator, while Ramin warns that over-relying on AI can stop juniors from learning to build.
“I still had to play that kind of data science orchestrator”
Survey results show students building parallel curricula, but cloud costs create a resource divide that industry credits help ease.
“The portfolio kind of has become a new credential.”
Northeastern is updating its curriculum in 2026, AI makes coding homework hard to police, and Ramin says industry must help academia.
“at some point, industry need to help academia”
Ramin advises early hands-on AI exposure and shares his excitement about a Reachy Mini robot for 2026.
“I would have never gone for robotic.”
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Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I am CEO at Prediction Guard, and I'm joined as always by my co host Chris Benson, who is a principal AI research engineer at Lockheed Martin. How you doing, Chris?
Hey, doing very well today, Daniel, how's it going?
It's going really well because have a close friend joining us on the podcast today and a previous guest. We went through the Intel Ignite accelerator program together and in different companies. And yeah, just really excited to have with us, uh, Ramin Mohammadi, uh, with us, who is an adjunct professor at Northeastern University and also lead principal AI engineer at Ibais. T Welcome Ramin. It's good to good to see you again.
Yeah, thanks, Dan, and uh, Chris, it's always, uh, great to be back.
Yeah, yeah, I, I've been excited to talk through these things. And even, uh, even before the show. Obviously, you're kind of living in two worlds, you're living in the industry world, and you're living in the academic world, and you've kind of been living in those two worlds for quite some time. Which is interesting because you have a perspective on, like, how, for example, data scientists or AI people, or machine learning people are being trained and what those people are actually doing in industry.
Which I find really intriguing, especially because so much has changed, I guess maybe that's a a good initial question is, is my perception right? That, like the role of an AI person, or a data scientist, or a machine learning person in industry, like like that. The day to day life of that person has really changed dramatically over the past even few years, and I'm curious if the academic side has kept up with that.
Yeah, so I think that that's an interesting question. I I think we need to break it down into multiple sections. Because, uh, I mean, uh, let's just start 1st, uh, do a quick review of what has happened, you know, where? Because we are talking about the complete transformation of the AI and data science job market, you know, I mean, uh, if you remember, and it, it was about like a decade, decade ago, back in 2012, uh, Harvard Business Review, they called data science is the sexiest job of 21st century.
yeah, that's why I got into it, because obviously that describes what I wanted to be, right.
That's right. And if you think about it, that that one phrase, it kicked off a massive gold rush. Everyone wanted it. Universities were spinning up the new master programs overnight, and the promise was pretty simple get a degree and learn a little bit of machine learning, and you're set, you're instantly employable. That promise feels like almost like a myth now, you know, I mean, if you talk with uh, any new graduate today, especially someone looking for their 1st role, uh, the feeling it's totally different.
It's brutal, the market is absolutely brutal. We we see job posting for entry level, you know, that that job requires about three years of experience. The demand has changed, it shifted fundamentally. It's not about what do you know about the, you know, from the textbook anymore, it's about what can you build, can you deploy and maintain a re real, um, like a scalable AI system. It's kinda like that's the new currency of hiring.
I think one time, Chris, I don't know if this was if this was us that came up with this discussion. But I remember quite a while ago we talked about kind of like full stack data scientist or or something like that. The idea being like you could figure out what kind of modeling you needed to do. You could do the prototyping and POC, but you could also like deploy something to actual cloud environments or something like that.
I mean, that seems like quite a tall order, Ramin, because you're basically saying, like, be a software, uh, like a proficient software engineer, but also be an infrastructure person, and also a. And there's this, I don't know. I've heard a lot of people say there's not really like a full stack engineer doesn't really exist. So yeah, is it? I? I guess? From that perspective, how much of what a data scientist or machine learning or AI person fits into those different buckets at this point?
Whether it's software engineering or infrastructure work, or actual, like knowledge of differential equations or statistics, or something.
I think I think that's also a great point. So if you think about back to data science job, the idea of data science job was that, uh, your job is kind of done. Once you got a good score in the notebook, you know, it's the the the, you know, that the classic. My model has ninety five accuracy on the test data, and you're, you're good, you pass it to someone else. And then, if you remember, I think it's around 2020s, uh, with this, um, this.
Some resources like, uh, Google Cloud, Rules of ML Ops, you know, it. It re laid out these new realities that successful ML needs a whole suit of real engineering escapes. The things like containerization, with Docker, CI, CD pipeline, automation, monitoring. And you know, you have to know if that your model actually works in the real life, and then you need to monitor it. Then after you deploy it, you need to basically look for the drifts, you know?
So industry made it really clear that job wasn't just build the model anymore. It's kind of like a you need to own the pipeline. So, uh, and then if you think about it, all of a sudden, the analyst or data scientist went from just being a simple analyst to being an engineers who build and maintain the intelligent systems. And so just as that engineering bohar was being raised by MlOps, along comes the 2nd, maybe even bigger tidal wave, you know, the generative AI.
And that becomes like, around 2023 explosion that you can see in the A Stanford AI Index. Basically, they mentioned that this was not just a cool new tool, this was an automation event, you know, it immediately attacked the entry point in the field that you they could do those jobs. Basically, you know, it's kind of so this. Shift was drastic from for the from the data scientists to ML Ops engineers, and all of a sudden AI, basically,
In addition to that, there's so much more diversity in, you know, as we were talking a moment ago about the notion of the full stack engineer, especially at the entry level, trying to fit into this, and the notion of like, what is full stack is changing fairly rapidly. There are a lot of different options out there. And not only do you have to try with that entry level student, have to try to fit in to the notion of what, you know, an organization is looking for.
But there's all these variations on that, and if they're not in the right variation of what that organization is looking for. In terms of this abundance of skills that are required for that given position, they're still out of luck. I mean, it's really a crapshoot for students today in terms of trying to find the right fit and represent that, represent their own ability to fit to the organization that's looking to hire. I'm really glad that I'm not out there in the job market in that way right now, it would be brutal.
yeah, so I think I I think that's, that's true. It's like, if you think about it as this AI wave comes in and this series of automation tasks, basically, uh, obviously, AI made certain things simpler. Uh, those are like the types of tasks, like a boilerplate task that you always used to give to the new hire. Basically, you know, it's kind of like the groundwork. And and for for an, uh, for someone as an early hire, a recent graduate, those w those type of job were kind of like the 1st step on the ladder.
How to, for example, you write a complex SQL query to get the data, make simple Python and get your hand dirty with the company's data. You learn about it, and also you show your skills, you know, and uh, but now it's n no longer like that, so you need to basically find the correct fit. What they exactly want, what they want to build, so I show that I can build that. And there was this study, uh, from OpenAI and University of and, uh, of Pennsylvan, Pennsylvania, uh, that they look at this task exposure to large language model and the take takeaway that they had was pretty simple.
Any repeatable task that used to be given to juniors, I highly vulnerable to, uh, AI, basically, and and automations. So if a junior analyst used to take all this afternoon, write the SQL queries and make the dashboard, now AI can just write it with a great prompt, right? So, uh, the basically the the economic case for hiring a big group of, you know, trainees and have them to do the work. And has ever evaporated, you know, there's kind of like a change.
For example, I used to hire lots of interns, uh, to basically help with the development and speed up the process. And since AI shift, to be honest, I just don't use AI for all of those tasks, you know, so, uh, so this has been this, uh, a big change. And of course, you know, uh, we. We are seeing this shift in hiring strategy, kind of everywhere, in big tech or even in, uh, startups. They just stop hiring for potential and they are starting hiring for proven capabilities.
It's kind of like the the the paradigm has changed. No companies these days basically afford to bring in 50 juniors or spend spending couple of years to train them, you know, they rather to hire five or maybe ten people that already have built or developed some complete system from day one. So it's kind of like, if you think about it, the new entry level jobs is technically what we would call mid level engineers. Couple of years back, you know, this shift is really bad, and with this, uh, kind of with this new bar, it's it's not like that, you know, you don't need knowledge.
So all this, you know, deep statistical knowledge, Python skills, they're all essential, but they are just at this point, they are kind of prerequisites. They are the ticket to the game. They are not how to win it. You know, it's kind of it's, it's, it's. You need to prove that you can build the company wants, what you b build, and then, you know, you go for hiring.
I'm wondering, because that bar has been raised, like you say, the kind of mid level positions that we used to call mid level, or maybe the entry level ones now, how does that change? Cause, I mean, I maybe this is a negative view that I'm about to give, but I'm very pro, you know, higher education. But I also think, like, even whether you look at computer science or data science sort of education. A lot of that does not.
Even before, before the recent shift that you talk about, it didn't always connect to what you were actually going to do in your day to day work, right? So now, not only does it not connect to that entry level kind of day to day work, but does it now even increase that divide? Where, like the like, how, how could we possibly train people to come in as mid level kind of data science folks? Cause I think, if what you're, if I'm interpreting what you're saying correctly, it's not that AI is making data scientists no longer relevant, or AI or machine learning people no longer relevant, it's still very relevant.
It's just the stuff that entry level data scientists or machine learning people used to do. And kind of, you know, level up on, that's no longer available. So where are they? Where are they gonna do that? And is it even reasonable for us to think that universities could help get them up to that level? I guess
Yeah, I think so, I, I would, I will answer to that question in two, two section. I think one part is about where is academia stands right now, and then, uh, then 2nd part will be talking about the industry versus academia right now. So, uh, let's just start with the where. Where does academia stands? You know, um, if if you think about it, I, I kind of call this, I don't wanna be negative, it's edu, I call it educational bottleneck, you know?
And to be clear, 1st thing is that, uh, you know, the faculties that we have in CS, ML, data science department, they are all brilliant, you know, they are like world class at teaching the fundamentals, the math, theory, history, the research. And that that foundation is non negotiable. You, you need it, but the curriculums often just stop there. And, uh, it used to be also kind of like that. And you still have the theory and leads leaves.
Basically, this huge gap between what a student learns and what employers actually need for them to do on the 1st day. As an example, you know, a student might spend a whole semester learning about the math behind all sorts of like optimization, back propagation techniques and stuff like that, and which is necessary. But as soon as they graduate, they basically see this job market that wants them to deploy on the Kubernetes, or they know how to work with all different cloud resources.
You know, so they know exactly how the engine works, but they actually never tried to drive a car in the traffic. And that, you know, um, there, there was this new post, uh, by Andrew Ng recently that he argued, Um, this urgent shift in the education, and I'm going to paraphrasing what he said. He said, knowledge is great, but skills are greater. Meaning that in the field that's moving this fast, you have to teach the practical skills to get the work done.
You know, you need to basically give the capacity, uh, to get meaningful work done by having a proper knowledge and proper training. So, uh, this is exactly what the job market is selecting for now. So that's, that's, that's, that's the view that I have on education at the moment. And uh, the the 2nd part that we can basically talk about is like a comparison between where is like, where is industry versus, uh, academia? And there there is a really good, uh, basically study by MIT, uh, recent study, basically that the the the stats are staggering.
Basically, they say that the uh. Right now, about 70 of the AI PhDs are just skipping academia and go to job market, go to basically industry directly, and that's a huge brain drain for the universities, you know? And the 2nd is that, which is the real killer, basically. And probably you, you, you, I'm sure, you know about this. Like, 96 of the major state of arts systems comes from industry labs, not from universities anymore. So the university is already falling behind.
And then companies like Google, Meta, OpenAI, they are the one that defining the frontier. Now, they are building the tools, they are setting the standards, and that's the absolute core of the bottleneck. Academy curriculums moves on a cycle of years. Getting a new course approved, like updating a textbook, it's a slow. By the time a university approves one new course to be like, let's say, for example, LLM application course, to be added to curriculums. The tools have already changed three times, you know, so the entire framework is really different.
Because, you know, it took a while, and that has happened to me also, like I developed course and take years to get approval to teach that course. And then you need to go back and update everything that you were planning to teach, because, you know, the industry has changed already.
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so Ramin, I, I love, I love how you highlighted this kind of divide between academia, industry, like what that is in reality. Anecdotally, I remember, uh, actually, last, I think it was last year, or maybe a year and a half ago. I lived by Purdue University. I was like, walking through campus and, uh, they were just finishing their. They had this new building, right? And uh, so this was 20, whatever, 2024, right? And it said, like, Hall of Data Science, right?
And I thought, and then my immediate thought in my mind is like, in 2017, you could have created a Hall of data science. Now you need a hall of AI. Like you, you're building the wrong hall. Uh, to their credit, I think they actually, so I, I just looked this up while you were talking. They did rename it Hall of Data Science and AI, so they, to their credit, they, they caught, uh, they at least caught up with the name.
But um, but but yeah, I guess, like, obviously you're an educator, and so you see that there is value in trying to have these. You know, formal, you know, form. Formal education serves a purpose and is different from maybe on the job training. W What do you think, uh, or? Or? Have you seen examples where this sort of practical skills are built up in an academic environment, rather than just kind of the theory or the or the knowledge?
As as you were kind of drawing the distinction there,
yeah, so, um, actually, um, that's something that we have been doing for the almost the last three years. So I ha basically developed this course, this ML Ops course at Northeastern University, almost three years ago, that we have been, uh, uh, ongoing. So the idea was this, this is like, uh, about three, four years ago. I was the hiring manager and I used to do lots of interviews for the our team. And, uh, I always, uh, basically interviewed smart, motivated, you know, good school, basically, um, uh, candidates.
But most of them struggled with the same thing. They understood the theory, uh, but they couldn't build anything, they couldn't ship anything, you know? And, uh, that's when it clicked for me that okay. If the industry, I personally, as someone who was in the industry and in academia, expect these students, or these basically candidates, to build a real system from day one. And then I know in the industry, we don't teach them that. Could we do something about it?
So I started working on this course, I built this, uh, ML Ops course, that, um, every semester, right now we have about 150, 270 students within one class, like a huge classroom. And instead of just learning the concept, they start by choosing a domain that they actually care about healthcare, finance, sport, robotic and whatnot. Then, as a team, they spend the entire semester on building one real product, and this real product. It's not just homework assignment, it's not a toy example, it's a real working system with deadlines, milestones, deliverable, just like a real, like an actual ML and software team.
And the best part of that is we wrap up the semester, you know, the way that we wrap up the semester is that the students basically present their product at our ML Ops expo. Uh, which is a full industry partner event we have been holding over the last, I think it's two years now, uh, this year, for example, uh, we partnered with Google, uh, so we are hosting on, um, in two weeks, December 12th, at Google Main, Uh campus in Boston, uh.
And where our students are pretty hyped to come there. But they would basically what they do, they show, they demo, uh, the actual product that they have built, uh, and uh, so the whole course is simple. Uh, you don't just learn ML anymore, we teach you how to build with it, you know? And, uh, the idea for me was to give the students this hands on experience that companies are looking for right now. And honestly, watching the students go from I have never deployed anything before to me and my team, we built a real product this semester.
That's kind of like the best part for me.
one at least hypothesis that I have here, which I would love your opinion on, Ramin, is on one side. You have highlighted how this kind of gap is widening, even like the between the theory and like, where you need to come into a job, like at a mid level. At the same time, this revolution of gen AI has been happening, which in some ways, to your point, some of those things are the things that are being automated by AI.
But it's also enabling maybe this, like younger generation of software engineers, AI people, to actually perform at a higher level out of the gate, but in a different way. So not like there's kind of a burden on maybe us as prior generation, data scientists and machine learning people, to understand that students and new hires need to, from the start, be doing their data science work differently. So just by way of anecdote, we were talking about this a little bit before the show.
That, you know, my wife owns a e commerce business. Black Friday Cyber Monday just happened. I, you know, day to day in my company, you know, I'm not doing as much kind of hands on work on the product as I was given my role as CEO. But it was like it was nice to go back. So for like, four days, I helped them during the sale. And I just sat in a room doing like customer lifetime modeling and like updated forecasts for 2026 and looking at churn and analyzing like customer journey and all this stuff.
And number one, it was a ton of fun, but I was, I was kind of coming at it from that perspective and kind of re entering some of those things that maybe I hadn't done as much for for a little while. Or, even, you know, maybe since the previous year, when I helped them with forecasting. Like, I was able to get tons of that done so quickly because I was having AI, honestly, write most of the code for me.
The thing, though, was I still had to play the data scientist to get from like point A to point B. There was no way that, like, I could have just said to any AI system like, Hey, I want. Like, write a three sentence prompt and get out all of the, you know, lifetime modeling and forecasting and all this stuff. I still had to play that kind of data science orchestrator and know what the things were, know what, you know, modeling techniques were relevant, know maybe what trade offs were and and other things.
So do you think, on the one hand, it's maybe depressing that the academic kind of industry gap is widening, but on the other hand, maybe there's? Am I right? That there's an opportunity to actually, like, lean in for for these students in terms of different ways of their of working, to, like, get to a a higher level faster.
I'm not sure about the higher getting to the higher level faster part, but just, uh, I. I saw, uh, a new talk recently by Neil Ahoin, Um, over at Google. And he made a great point that about this data science job, and he basically was saying that the, uh, the data science job is, it's not gone. But AI is just forcing them to change dramatically. Uh, it's, it's no longer is about analyzing the data, building certain, you know, set of dashboards and stuff like that.
As we say, you can just with the knowledge, just prompt it properly and just having the data and just build that quickly, you know, uh? So there are certain certain types of tasks that you used to do for. Try to climb the ladder to learn more and more, but that they are not the same anymore. And the expectation is not for you also to do the same task. Because, you know, uh, if company is hiring, you probably this days they want more.
Um, but I think it is a really great point that for hiring managers, or for someone that, uh, when you hire someone on your team or have someone new juniors on your team. You need to also account for helping them to, like mentoring them properly, to to be sure that they can evolve and learn. Otherwise, we basically take this cognitive ability from them because they everyone. If you just ask everyone to just build, build, and they just use AI, they don't.
They're never going to learn basically how to build, so we take that cognitive, uh, ability away from them to just build new, P faster products.
Yeah, I think you're really onto something there in terms of, you know, like, one of the things that that I have done for the last few years is, uh, is I'm a capstone sponsor for capstone projects at, uh, at Georgia Tech, uh, in the in the College of Computing, and so, uh, as, and I'm doing that from my nonprofit role, as opposed to my day job. And so when I work with different teams there, I think one of the challenges is they're kind of bringing what they know, you know, certainly, Gen AI capabilities have helped them, you know, step up a little bit along the way in terms of figuring out.
I think the areas that I've noticed that they're still struggling, the students are there's, you know, going back to to Dan being a data scientist over the weekend instead of a CEO in that moment is he's bringing all that business knowledge, you know, years and years and years of business knowledge and understanding about what's really needed. Uh, in that, and I think that's, you know, that's one of those things that is is part of the struggle with junior level is is there's the the kind of concept of.
I've learned tools in university and I'm trying to bring them to bear, and they're not always the right tools for the organization they've joined. And they don't necessarily know how to combine that with all the other, uh, all the other tie ins that that organization may need. That were not necessarily something accommodated in their in their academic development. And so, you know, that's kind of exacerbated by the fact that now, with gen AI kind of replacing a lot of those junior roles coming in.
And and you know, how do you? how do you ramp up? It does seem, to your point, like things are actually getting like, even though we have new, amazing tools in in the form of gen AI capabilities, it seems like things are getting harder to bridge that gap. And and um, I'm not sure how you do that, how? Cause? It's a combination of both, kind of the the experience of being in the real world, along with fast moving, you know, a fast moving technical landscape to navigate.
Are you seeing that from your side with students, and and how are you tackling some of those those subtleties that are there?
Yeah, actually, definitely. So, uh, two weeks ago, I, um, sent out a survey to my students and I asked them basically to take a couple of questions. And I specifically did this for our talk. And uh, so as part of the survey, basically, there were some questions, and uh, one question was, um, which is 60, basically of the of the students. They said that they are taking online courses on top of what they are taking in in the school.
And another question 82 of the students say that they are participating in hackathons in order to learn to how to quickly to build. And about 46 of the time they are attending workshops, you know? So they are building their own parallel curriculums through side project, open source contributions or certifications through AWS, Google, you know, and that's exactly it, you know? The portfolio kind of has become a new credential. It's no longer about your grades, it's not about what you have as a portfolio.
And this is also important for us to. It's kind of like a dose of reality that this self learning path isn't easy and isn't equitable. You know, it takes tons of time and costs lots of money. And if you want to practice building a real production grade system, working with the cloud service that always costs money, you know, it's those commercials. And how many students, like, if you think about students already paying one zero tuitions, they cannot also afford hundreds of dollars per month for cloud computing, you know, to practice.
So it's kind of like a huge shift. It creates this resource divide, and at this point, I think the bar isn't just higher, it's kind of also financially more expensive for the students to learn. And right now, for example, uh, shout out to our friends at Google, you know, they give us lots of credits for our ML Ops course every semester. Because, uh, our students, they can't otherwise build anything in a real world. And I personally reach out to lots of UH providers in the industry and I say that, Hey, you know what?
We train these students to use your tools, give us some cloud credits so they can basically learn and they they build upon it. But yeah, so that's that's my take on that.
Well, Ramin, I am kind of intrigued because, well, on the one side, you're thinking very in a in an innovative way about how to bring this kind of skill or or reducing this skill gap. Being creative in in the academic setting to get people these skills. But also, you know, you're a practicing AI engineer, what have you seen, kind of personally? Cause you, you're already operating at a higher level. Um, are there also changes? Uh, s any like significant changes that you've noticed in your day to day work over the kind of past few years that have caused you to think about your day to day tasks?
Uh, differently, uh, like, more so than the entry level type of folks. But actually ways that, like you, you're fundamentally thinking about, like your workflows, or or how you're doing those kind of higher, maybe higher skill or or higher level kind of data science, AI stuff. I'm I'm wondering if anything stands out for you.
Yeah, definitely. I mean, I, I personally have been part of this shift. I started my career as a data scientist, then, like in 2020 eighteen, I started as an ML engineer and it just went up. Then last year I started as an AI engineer, so I also have been part of this change myself. Data ML AI Exactly the same pattern. And, uh, for me, when I look at them, they are kind of similar. If you put the data science aside, because that was kind of like there was no production, there were lots of research, especially around it.
But when you go to ML and AI, just the terminology is different, they are technically kind of similar. I think the main difference that I personally felt is that I need to in my day to day work. To work a lot with LLMs because as a requirement for certain things, and work a lot with the larger models, which requires you to have a better understanding on, you know, this kind of like a GPU optimization, how to break it, break your models and basically ensure that they are optimal, basically.
And those changes, you know, it wasn't something that you do maybe a couple of years ago. So I ended up personally try to read a lot, you know, spend summer time, just read different books to learn, to advance my own career. And I always talk about this with my students. When I learn something new, I bring it to the class. I was like, Okay, I was recently basically reading about this and this was really interesting. This is the link, and maybe I sometimes give them a small lecture also on it.
But I think, yeah, so it's like the change is there for everyone, not just for a junior. It's like it doesn't matter if you are a principal or a junior technically, but who's getting being more impacted? I think that's the part that's kind of like, unfair, you know, to to the to the juniors technically, or or recent graduates.
I'm curious to extend this out a little bit, you know, as we kind of went from the challenge of juniors and Dan introduced, you know, the challenge of kind of us, you know, as as as people who are past that point in their life. But like, we have fast coming, you know, fast changes are coming, even more. In the sense of like, we're hitting that point where physical AI is really on the rise now, you know, not just in certain industries, as it has been historically, but in many industries.
It's expl, you know, it's exploding outward at this point. And we all have challenges in terms of incorporating these new realities into what we're doing and and how we're gonna learn about it. What does that imply at the university level? When you're getting back to students? And and they're already, you know, you're already trying to bridge the gap into the corporate world or the startup world, or wherever they're gonna be productive. But you also have this explosion in terms of the places that AI is touching in new and different ways.
What are the what are the implications on the curriculum and on the the burden that professors have to try to get their students ready for that next thing, which is steamrolling over us already?
I think it depends, so let let me just. I know some other schools are doing that, but I'm going to speak with respect to Northeastern, for example, Northeastern, Khoury College of Computer Science. Um, the as of this year, basically, 2026, they're updating their curriculums finally, so they and not everything is going to be a small shift, but gradual, basically. So they're, uh, introducing some more practical, uh courses into the curriculum, and they also, for example, they're weaving the ethics directly into the coding part of the curriculum.
So, you know, but this is going to be kind of like an a slower shift on on the curriculum side. But on the other end, from the teaching perspective, you know, uh, and this is like, kind of like AI is kind of like a double edged sword at this point. Because, uh, students, you know, they all use AI, they are using the generative AI, so they basically, which is great. Uh, I would tell my students, you know, use it, but don't lose to it, you know, kind of like you need to use it, don't lose it, uh, so it's kind of like you need to be sure that you can learn, move faster with this type of thing.
Not to just give away all the autonomy and you just basically you just use them for everything. And uh, so, and then from the other end, from the teacher's perspective, it's it's kind of difficult. Because when you give, for example, homeworks or labs to students, which is especially coding, I'm not talking about writing an essay like coding perspective, you don't know. You can't even tell that if they wrote the code or not. Everyone return great codes these days, you know?
And then there's the homework and there's no way for you to just say that like, uh, if it's written by AI or not. You know, they are really smart, like in how to, uh, like, change the temperature to ensure that the the the result is not being detected. So, uh, so, so again, this is like kind of like a double edged sword. But also from the other end, it's like, because there are lots of information, lots of changes in the market, in the industry, in the domain.
Every day, like every day you read the news, there's a new article, there's something coming out, and it's hard for basically academia to keep up with that. You know, it's like, are you? Academia is falling far behind the industry and it's going to just this gap is going to just expand the way that it is. And I think at some point, industry need to help academia, it shouldn't be just academia need to keep up with the industry.
If the industry needs new talent to come later, you need to step forward and say that, okay, you know, let me also help them, let me start some program, let me participate in some of the courses that they have, you know. So otherwise, it's kind of like a chasing a ball, like a academy, just constantly trying to keep up, and that's not going to win.
That's fair, and I think that's a good notion that I think, uh, industry really needs to consider is in investments back. I agree, I think it's been largely a one way street there. I would like to flip a little bit the timeline around to the students that are coming in. So, and I'm asking this selfishly. I have a 13 year old daughter in 8th grade, she is. We are. We have been applying to magnet schools and things like that and getting her ready for her high school experience.
And she has never been someone interested in AI, that was dad's thing and all that. But as she has started looking at what she wants to do, she's starting to recognize that whatever that is, AI will impact her in a significant way going forward. So it's not just the kids that are focused on technology at this point, but all of the kids. And as she does that and they're entering into high school, what advice do you have for what high schools need to do before they come to you?
Before you're getting those students and you're trying to prepare them for industry and a career, and moving through their lives, you have students coming to you. What would you like to see from high schools in terms of how they prepare these kids? To be better or more ready to come into your care as a professor, so that you can do the thing that you do?
Yeah, so I think I think that's a great point. And um, you, you have. There are already two shifts I have been spoken, like, uh, by neighbors. Similar question that, hey, my kids, should they go back, go to college for, uh, computer science anymore, should they study this anymore? And I think the answer is that, yes, you know, there, there, there will be shifts in the market. And it's. It's not just computer science, it's not just AI.
AI is going to impact so many things, some some areas like is slower, but some areas much faster. And at some point, all of us basically become somehow we need to learn how to work with AI. And I think it's really good if from high school you understand the concept, not not maybe the math, the theory behind AI, but just to learn, okay, in general, what, how does AI work? There are lots of AI capabilities that you don't even need the math behind them.
You can just build a system just by knowing how to put the components together. So if they could, like from high school, go to part of the workshops or participate in some sort of like trainings that build something simple. You know, that automatically opens lots of door, like a thinking process for you for the future. As you go to, like, uh, after high school, and you want to go basically to universities and you learn in different courses, different concepts.
You're like, Oh, I know, maybe I can build something around this, you know, I always think that everyone can be an entrepreneur, you know? It's kind of like, as long as they have the correct mindset and the energy for it. So if if they already have been trained from high school and they have not not trained again in a bigger way, just kind of this easier way of training, like teaching. They could potentially advance more in university in compared to students that they just want to learn it during the university.
Well, I know that we've talked a lot about kind of a a lot of perspectives, both from the industry side, from the academic side. I think all of us on the call, though, are generally excited about kind of certain parts of the ecosystem, the way that they're developing. Um, from that side of things, as we get closer to the end here, Ramin, what? As you look at the ecosystem, cause you're again, you're, you have multiple views of this ecosystem, from the industry side, from the academic side.
What? What's most exciting for you as you're kind of entering into this next year? And maybe it's something like, Oh, I can't wait personally to, you know, have the time on a weekend to explore this, or maybe it's something you're you're already getting into.
Yeah, definitely, I'm actually, uh, I j um. Recently purchased the Reachy Mini by Arm.
yeah, yeah, yeah, the robot, right, the little, uh, it's kind of a desktop type robot, right?
yeah, so it's, uh, I'm, uh, so I'm pretty excited and waiting for that to be delivered. I think it's the delivery is going to be early January, hopefully, fingers crossed. And I'm pretty excited to work with that and build some capabilities I have in mind. Uh, and when, when I think about all these changes, like, if you would put me back a couple of years ago, I would have never gone for robotic. I'm like, Ah, no, you know what, it's not my thing.
But now, with this AI change, and I already went through the, you know, contents of, Uh, Unhugging Face, which is this guy's like, great reading through the documentation. I was like, Wow, that's pretty straightforward. So think about how much AI or changed the field that I can easily go buy a robot, like a small robot, and I'm planning already ahead of time. They also have this simulator, so you don't need to wait for it to deliver.
You build ahead of time, the apps and simulate it that it will work on the robot. And when the robot comes, you deploy it. So that's my go to, like, a what I'm excited for in 2026.
Yeah, it's kind of crazy. I I. I feel like when we started in this field, it was like, hard enough to get the dependencies installed for Tensorflow and just be able to run any model. Just like that, in and of itself, was like,
Are you? are you trying to give us PTSD? Is that? Is that the goal I know, here?
yes, yeah, it's like, regardless that that is, that was the hardest problem. And now you can, like, have a whole sim, like digital twin of a robot, and like, do all that. It, it is pretty spectacular. Yeah, well, I, I'm also excited for that. I think we do have one coming here to our offices as well. So, um, I'm excited to see what that's like. I've never done any robotics, uh, really, other than maybe those like, what, what are those, uh, Lego robotics sort of things?
But uh, but yeah, excited to, excited to see where things are going. Thanks for sharing some of your insights with us, Ramin. It's been, uh, it's been a real pleasure, uh, and hope to have you, uh, on the show, uh, 3rd time to let us know how the robotics went.
Yeah, I appreciate that. Thanks for having me again. Uh, and um, was great.
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