Current Trends and Challenges in the Tech Industry

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Q: Hi Matt, thanks for chatting with us. Could you tell us about the current state of the tech industry and the challenges you've observed?

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A: It's been tough due to market changes, over-hiring during the pandemic, and technological shifts. A lot of tech workers were laid off in 2023 and 2020, and while things are a bit better in 2024, it's still rough. I had a job interview recently that I hope works out. For anyone wanting to enter tech, I'd say we're in a tough spot right now. If you're passionate about it, go for it, but know it's not the easiest career path right now.

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Typical Day at Work 

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Q: What does your normal day-to-day look like?

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A: As for my day-to-day, I used to work at Deutsche Bank before the pandemic, then remotely for about a year. The daily routine varies depending on whether you're at a big company or a smaller group. Larger companies often have separate departments for data engineering, but in smaller teams, you handle everything yourself. I’d usually start with emails, figure out project objectives, attend a scrum meeting, and then work on coding or statistical tasks. How long it takes depends on the project—it could be a day or weeks.​

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Matt's Career Journey Overview 

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Q: Can you give us a quick overview of your career journey so far? What college you went to, what got you interested in your current work, and any previous jobs related to your startup?

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A: It’s been a winding road. I went to the University of Rochester in the late 2000s. I originally wanted to go into politics—my degree is in political science—but I had strong math skills, and there’s a lot of math in political systems. After two years of trying to land a stable political job, I switched paths. I took some engineering courses at Columbia University to prep for grad school, and during that time, I got recruited to work at a company called Epic Systems in Wisconsin. After two years there, I returned to New York, went to Columbia’s Data Science Institute, and later worked at Deutsche Bank for a little less than two years.

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I also did some freelance work, including with a guy dealing with Facebook ad revenue and a small oil company in Texas. I even worked with a company called SafeGraph that handles geospatial data. I also spent a year at Instabase, a company focused on automating data entry, essentially extracting data from PDFs using trained models. That’s been my journey.

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Emerging Job Opportunities in Data Science

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Q: Given the tech industry's current challenges, where do you see career opportunities emerging in the next 5 to 10 years?

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A: The role of a data scientist isn’t what it used to be. Most people will need to specialize—whether in machine learning, natural language processing, data engineering, or DevOps. I focus on machine learning, but the industry is shifting. For example, tools like ChatGPT can now handle basic and even somewhat advanced coding tasks. If AI can do basic programming, I’m not sure where software engineering will go. I don’t think the industry will collapse, but it will definitely change.

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There’s also the misconception that creative jobs can’t be automated. But we don’t live in that world anymore. For instance, back in 2019 at Deutsche Bank, they laid off tens of thousands of people because they didn’t need humans to manage portfolios anymore. So, moving forward, it’s crucial to understand how AI works, especially large language models. Also, learn as many coding languages as you can. Python is a must, but SQL and newer languages like Go are important too.

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Advice for Aspiring Data Scientists

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Q: Do you have any specific advice for a young person interested in your field? Any work they should get involved in before becoming more advanced?

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A: Well, learn to code, and keep an eye on emerging fields like healthcare tech, which is growing. Healthcare is one of the sectors that’s still hiring, even as others like finance or startups struggle more with the current economic environment. Tech in healthcare is becoming more important, and that’s a solid area to look into.

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Career Reflection

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Q: Looking back at your journey so far, is there anything you would have done differently?

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A: Oh, yes, I mean, had I? Okay, yeah, I mean, like, had I? If I knew I was going to be where I am today. Well, I would say I wouldn't do—I wouldn't have gone into politics, but then I wouldn't have defended a lot of my friends. But that's a personal thing. What I would say is what I would have learned to do back in college 15 years ago. I’d have learned to code. I didn't know how to code until like 10 years ago; I had to teach myself how to code a lot of stuff, and I've been doing a really good job with it. It’s just that... I know, saying, “learn to code” is cliché. But I will say, learn to code if you want to go into tech. That's important, and you never know when it could be important in another field. That’s definitely one of them. Well, that’s the only—that's the only thing I could say I would do differently now. I mean, anything else I would have changed is totally personal and not relevant to my career path. But what would I—what else would I do differently? I probably, if I still did politics, I probably would have pursued grad school a little sooner, maybe. But it is, but it is a—but it is surprisingly competitive. And yeah. Which is weird, ‘cause you think it’s not gonna be competitive, but like it’s even more competitive now, because so many of them are out of work. So... I can say. The thing is, a lot of things I would say, what would I do differently? I said. A lot of it’s personal. But other than to learn to code and get in on the ground floor while you still can. But that’s just me. Knowing what I know now, there’s no way me as an 18-year-old in 2008 would have known all this would happen.

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Matt's Question Highlights the Importance of Cutting Edge

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Q: Matt asks: Are you guys planning to go into this field?

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A: We answer: Well, I think the goal of what we're doing is to look into emerging fields. As technology continues to advance, there will be a lot of new and different careers coming up. So, I think it’s important for people to be informed about what they need to know to be involved in these upcoming careers. Speaking to people who are currently in these fields and getting their advice is really valuable.

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For example, you mentioned the importance of learning to code. That could be information someone might take and say, 'I was considering taking a coding class because I want to be involved in tech when I'm older. Now I guess it would be smart for me to do that.' So, yeah.

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Differentiating Data Science and Machine Learning

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Q: I know you have worked in both fields, so what would you say are the similarities and differences between data science and machine learning?

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​A: Data science is a field that studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods that utilize data to inform performance or inform prediction. So in other words, data science is basically the general analysis of data. Machine learning is when you are basically teaching machines, in other words, models, to find trends and connections within the data. That is—that’s really the simplest way I can put it. They are related; machine learning is a subset of data science, and AI is sort of—AI is sort of machine learning, a type of machine learning where you’re teaching a computer to basically have an artificial intelligence, as is the name.

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What is Cutting Edge about Matt's work

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Q: Why do you consider your work cutting edge, and how has it changed over time? Is it still as cutting edge as it was a few years ago?

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​A: It’s cutting edge because data science involves using an immense amount of data, which is collected everywhere around us. This was true when I started and remains true today. For instance, I think there was more data collected last year than existed throughout the entire 2000s. This enormous amount of data allows for more detailed analysis. In the past, businesses had to sample their customers and infer what the rest wanted due to technical limitations. Now, we can access complete transaction records for every customer, which is pretty amazing. Additionally, AI and data science have expanded into areas beyond my specialty, like curing diseases.

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The Role of Foundational Math Skills in Advanced Data Science Applications

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Q: We’ve talked to people who use data science in healthcare, like for insulin prediction and Alzheimer’s research. Do you still use high school math skills, such as calculus, and multivariable calculus in your work?

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​A: Yes, absolutely. Calculus is still important, especially derivatives and integrals, which are a big part of calculus. If you’re pursuing data science, you’ll need to understand these concepts because advanced statistical methods rely on them. High school math is crucial, but you’ll also need to learn additional topics in college, like differential equations and linear algebra. So, stay engaged with your math studies—it’s fundamental. My mom always emphasized that math builds upon itself, so the skills you learn are very important. 

 

Multivariable calculus as well is essential because it deals with multidimensional data. When I was at Columbia in 2014, I took advanced calculus that included concepts like shapes and partial derivatives. Partial derivatives might seem complex, but they’re essentially about taking the derivative with respect to one variable while treating others as constants. For instance, the partial derivative of 2x+y2x + y2x+y with respect to xxx is 2, treating yyy as a constant. It’s an extension of basic calculus concepts. While you don’t need to know every advanced topic, having a solid grasp of differential equations and linear algebra is important.

 

Pros and Cons of Coding with chatGPT

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Q: How do you use ChatGPT for coding? I’ve seen people coding with ChatGPT and was curious about how it works.

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A: ChatGPT is fantastic for coding. You can simply ask it to generate code for specific tasks, and it’s like something out of science fiction. There are two versions: ChatGPT-3.5, which is fast but has a few bugs and can’t run code or generate images, and ChatGPT-4, which costs $20 a month and can run code and generate images. If you use it frequently, the paid version is worth it, but even the free version is useful for generating code snippets that you can copy and test in your own development environment. However, always verify the results, especially for complex tasks, as it might not always be accurate. It’s a form of artificial intelligence that’s getting closer to understanding complex processes, though it’s still evolving.​

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Matt's Future Aspirations

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Q: What are your long-term career goals, given your current situation?

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A: It feels like a job interview! My hope is to eventually become a director of data science for a department. But honestly, I’m uncertain about where the industry and my career are headed. It’s evolving rapidly, and I can’t predict what it will look like in five years. I have aspirations, but whether I’ll reach them or what the future holds is unclear.

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