Live Q&A: How 4 Bootcamp Alumni Broke Into AI Careers

Jess Feldman

Written By Jess Feldman

Liz Eggleston

Edited By Liz Eggleston

Last updated on May 31, 2024

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With the release of new artificial intelligence tools and capabilities, technical careers are rapidly changing. We spoke with four bootcamp graduates from Springboard, General Assembly, Coding Temple, and NYC Data Science Academy to find out how they’ve expanded their tech careers into working with AI. These bootcamp grads are all working in different technical fields (data science, UX design, and software engineering) – check out their insights on how they’ve upskilled over the past few years and their tips for career changers now. 

Meet The Panelists:

  • Nate Welter went to the Full Stack Software Engineering bootcamp at Coding Temple in 2021. Before Coding Temple, Nate was working in account management and now he’s a Software Engineer at Notable. 
  • Jeff Appel started working in UX and Product Design in 2018 after graduating from General Assembly’s UX Design Intensive bootcamp. Before that, he was an aspiring academic and since GA, he’s worked with agencies, startups, and enterprise organizations. Jeff is now Lead Designer on a Customer 360 Platform team at Salesforce. 
  • Lou Zhang went to the Data Science Intensive at Springboard in 2016. Before Springboard, Lou was an industry analyst at a trade association. And now he’s been building his career in data from data analyst to Director of data science and is building out the data science team at Datacor. 
  • Luke Lin was in the second cohort at NYC Data Science Academy and then went on to teach there afterwards. He is now a ML/AI Researcher and Engineer at JPMorgan Chase & Co!

Beginnings in Tech: What Inspired Their Career Changes

Did you have programming experience before enrolling in a bootcamp? 

Lou: I had a little bit of programming experience. I used statistical programming applications in college as an economics and finance major -- Stata. a bit of Visual Basic, and Excel. It was a natural transition to Python and R because it was the same style of thinking. My past job did not involve a lot of programming.  

Luke: Before I joined NYC Data Academy, I was a Ph.D. student in pure math. In my degree program, we did everything in pencil and paper! I had no programming experience. 

Nate: I had almost zero technical background whatsoever! I had a bit of Excel experience during my undergrad at Indiana University. I had a major in arts management and a minor in business. Going to the bootcamp was my first time ever seeing code — I had a month of self-taught experience before the bootcamp. 

Jeff: Absolutely not! I couldn’t have told you the difference between HTML, CSS, Python, or how they’re used.

Why did each of you ultimately choose the bootcamp you attended? 

Nate: I used Course Report when I was researching bootcamps. The biggest thing that stood out to me about Coding Temple was the individualized attention. I had a relatively small cohort, about 8-10 students, so I had a lot of chances for 1:1 time as we went through the bootcamp. 

Luke: I took advantage of being in NYC in 2015 when there were a few options for in-person bootcamps. I went to one of their events and talked to people in different schools. When researching bootcamps, it’s likely you’re going to know what you want to learn from a bootcamp and you’ll be able to feel if they’ll be able to provide what you need through those connections. 

Lou: It was a combination of good SEO! I saw an ad on Facebook for Springboard, which piqued my interest. I did more research into Springboard and a few other programs, and what drew me to Springboard was that they had an individualized mentor program that met half hour to an hour every week with someone with industry experience — that was huge for me because I didn’t know many industry professionals at the time and I thought it would be critical to build my network. Springboard had more of a vocational job focus. In 2016, they didn’t have the job guarantee, but everything was geared towards finding that first job in data science. There was a heavy emphasis on developing a portfolio, interview skills, and networking. My goal wasn’t necessarily to understand the nitty-gritty theoretical underpinnings of data science, but it was to get a job so I could get hands-on experience with data as soon as possible. 

Jeff: Before I went to grad school, I was a copywriter and copyeditor for a design agency in Denver. I was doing marketing copy, but I was also working with designers on proto-UX writing and information architecture and looking at micro copy. I was also a project manager on the side for some of these designers. The founder of this agency took me and a few other designers out to lunch and said, “Jeff, you’d be a great designer.” I didn’t go to art school, so I didn’t know why I would be a good designer. A few years later, I was finishing my PhD and had a friend tell me about UX design bootcamps where I could learn immersively. He suggested General Assembly, so I attended a free meet-and-greet to figure out more about UX design. (Going to the intro classes and seeing yourself in that environment is really smart when looking for a bootcamp!) I was impressed with the overall air of what they were doing, and they had a good reputation. I knew some designer friends who’d recommended them. They also had a strong emphasis on networking — how to reformat your LinkedIn and resume so you’re actually making a step forward as a professional in the tech industry. 

Lou and Luke, you both chose a data science bootcamp, so did you have your sights set on working with AI? 

Luke: There’s a myth that if you put any data into a machine learning algorithm, you’ll get a perfect result. If you’re considering a career in machine learning (ML)/AI, then you need to understand data! Your understanding of data is crucial. When researching what bootcamp you want to attend, go to their events, interact with them, and see if what they say has any connection to what you know. If you have a quantitative background, look for the basics behind the algorithm and how they work. See if they have tools or methodology that allows you to do what you’ve been doing well, better. You are the one who will provide the answer; the tools are there to help you get to that answer more efficiently. Look for people you want to work with and learn from. 

Lou: I sort of knew I wanted to go into ML and AI. I knew that data science was a rising field, but I didn’t know the specifics of what made data science “the sexiest job of the 21st century.” I didn’t know what I’d be doing on a daily basis, which is where Springboard came in and helped. They immediately set me up with a mentor (who happened to be the first data scientists of Instacart in 2016!)  who had a lot of industry experience. This was an incredible opportunity that I wouldn’t have been able to hook up myself. Springboard was a critical part of that. After talking to him, I was able to see what it was like working in data science at a startup, getting that ground-level view of what that was really like. The coursework ensured I understood the basic fundamentals of what it meant to be a data scientist. Once I started my first job, I was immediately able to apply and use the skills I gained in the bootcamp. 

Learning AI at a Bootcamp

As a reminder, all of these bootcamps now offer AI courses: 

But there are many paths into AI — you don’t have to get a degree or a certificate in AI! Starting with the foundational skills that you learn at a coding bootcamp is a great place to begin. 

Translating Bootcamp Experience Into a Tech Career with AI

Thinking back to when you graduated, what had you actually learned in the bootcamp? What level did you feel like you were at on Graduation Day? 

Jeff: I knew just enough to be dangerous and competent at the job. It’s just like any educational endeavor: you get trained to do something and then you hop into the context and start seeing the ambiguities and gray areas where you have to think critically. I have these hard and soft skills to negotiate certain situations. I learned that I could do the job as-is, but I also approached it as a junior designer, so I was happy to find a space where there was a good team, a really strong creative director manager who had a 20-year career in design, and several other levels of designers who could take me under their wings and walk through different scenarios together. I got everything I needed to get into the room, but I needed that growth mindset where I could put those skills to action. 

Nate: Echoing Jeff, I felt I knew just enough to be dangerous. At Coding Temple in the Full Stack program we learned a very specific set of languages and frameworks, so I felt competent in creating a full stack application, given the tech stack that I learned. Aside from the hard technical skills, the most valuable thing I learned at Coding Temple was how to think like a developer and programmer. I learned enough to get my foot in the door, but once I did I saw the context around the role and the ambiguity — I’d developed a mindset to know what I didn’t know right then so I could figure it out with the tools in front of me. The bootcamp helped me use tools to find solutions on my own. 

Lou: I found a job before finishing the bootcamp because of networking! When I first got there, I felt completely unequipped because I hadn’t even finished Springboard and was learning on the job. Luckily, the team was doing experimental work and didn’t have a charter to immediately put out revenue-generating products, so a lot of what we worked on was very experimental, like a laboratory. That gave me a lot of opportunities to slowly and methodically play with different methods and learn on the job. That was critical in allowing me to gain experiential experience for data science for all my subsequent positions. By the time I got to my first data analyst job my mentor was able to help me with the work itself, which was extremely valuable. I had a secret weapon! My mentor (the data scientist at Instacart) helped me, and I’m really grateful for that opportunity. 

Luke: It’s a totally different mindset, but I vividly remember that by the time I graduated from the bootcamp I could do anything in data science. If someone asked me to give them a summary or data set, I could say “yes.” I’m more proficient now than when I graduated, but that mindset that I could do it was established. 

We have a couple of PhDs on this panel, and that takes time! The average bootcamp graduate has 5-7 years of work experience, and people who have invested time in developing skills in other industries are still very valuable to teams. So, from your perspective, do you think older people can take advantage of a bootcamp?

Nate: People of most any age can take advantage of a bootcamp. I took Coding Temple remotely. We had a logical thinking entry exam, but outside of that anyone who has a general understanding and knows their way around the internet and a computer could absolutely hold their own in a bootcamp! There’s a misconception that tech is just for younger folks as an industry, but that’s absolutely not the case. I graduated with and am currently working with people of all ages. If you’re interested in it and that’s the only thing you’re hung up on, absolutely take the plunge — you can definitely benefit the same as anyone else can. 

Jeff: I agree with what Nate said, that you can do a bootcamp at any age. What I’ve found is that if you are a little older, you probably have other industry, work, or life experience that you’ll be able to bring to the table as well. Especially in something like UX design, which is like herding cats! The drawing part is the easiest — it’s trying to gather everyone together that can be challenging. I was in my mid-30s when I went to the bootcamp, so I was able to take some of my prior life experience and bring that into the situation as a UX designer. Life skills are highly valued in our industry because we’re often looking for people who have had different experiences that they’ve been able to grow from. 

What was the most useful skill you learned at the bootcamp that you’ve been able to apply in your current role in AI?

Lou: For me it was data storytelling, and being able to paint a picture with your analysis and communicate that with technical and non-technical audiences. Putting things in relatable terms is an extremely useful skill in any sort of technical occupation because most of the people you’re presenting to will not be as technical or well-versed as you in that particular application. This also helps establish credibility. 

Luke: Credibility is very important in any position you work on. The most useful technique I learned at the bootcamp is visualization, or showing things with data. I work in a bank which is a heavily scrutinized and regulated industry so you can’t tell the governance “because chatGPT told me so.” What do you do when something that is counterintuitive happens when using AI? If machine learning tells you something that businesspeople don’t agree with, how do you convince them? You need to be able to spot abnormalities quickly and then aggregate information that justifies what the ML algorithm finds in a simple way. Grouping data aggregation becomes very important. The most crucial skill for me among stakeholders is credibility. 

Jeff: I didn’t understand before doing a bootcamp that much of a designer’s job is storytelling. Whether it’s based on research you did yourself or got, and then aggregating all this data and trying to synthesize it, make sense of it, and interpret what it is so that you can present to others. You’re talking to other stakeholders who aren’t designers and may not have much of a technical background. “Here’s what we found which is driving our decision to move forward in this way.” You’re trying to persuade them and help them see the broader picture through a story that you’re telling. 

Nate: In my current role there’s not a specific technology but I’ll echo what’s been said. We have a lot of stakeholders (project managers, customers, etc), so being able to talk about something technical to someone who may have little-to-no technical background makes all the difference. At Coding Temple, we had plenty of opportunities to present projects, do whiteboard problems… almost everything from day one through the end of the bootcamp is to get you talking and very comfortable around these technical topics that you initially had no idea what they even were. 

How Bootcampers Work with AI

How are each of you working with AI today? 

Luke: Since I’m on the retail branch and more toward the marketing side at Chase, we want to know more about customer behavior. Machine learning is effective at removing repetition. You don’t need to hire a dedicated customer associate to monitor what people do. You can use algorithms to capture those signals and make sure we interact with our customers at the right time. We have better technologies than they did in 2015. We can use Neural Networks (images, sounds, etc) and AI to interpret the results in a very human-like way, which opens up a lot of possibilities.  

Nate: I’m not directly developing our AI solutions at Notable, but I do work on the Integrations team so a lot of my job is integrating our technology at Noteable with our customers’ healthcare record systems. A lot of what we do is called RPA (Robotic Process Automation), which tells a machine how to do a task like a person would: logging into a website, searching for a login button, etc. It’s a mixture of OCR (Optical Character Recognition) and image recognition software. We have a dedicated AI/ML team that works on those products and it's my job to fit those into our existing data flow. Most of my job is interacting with that AI team, helping debug, bringing issues to them, and integrating their work into our customer solutions. While I’m not directly developing AI, the advent of things like GitHub Copilot and ChatGPT have helped me level up my programming career. Try to make as much use out of those tools as possible, whether it’s automating tasks for myself as a software engineer or leveling up and getting things done quicker. 

Lou: I just started at my current job at Datacor but at my last job we were working on an unsupervised machine learning model to predict failure rates on machines. We used methods like Principle Component Analysis and clustering methods like DB Scan to determine clusters of similar activity. Outliers from those activities would constitute potential anomalous cycles on these machines. This was a pretty novel method and we were able to patent it! It was a very interesting exercise in not just coming up with a new algorithm but also walking through the entire process of patent as well: what it felt like to interact with lawyers and legal to get this formalized as a new invention at the patent office. It was cool to be able to do this just a few years out from graduating from a bootcamp, knowing that just a few years prior I had no knowledge of machine learning! 

Jeff: In design, I’ve worked on both sides of it. At Salesforce, we have a proprietary generative AI called Einstein CoPilot, and current work is integrating that into my area of the product to help out our personas so they can get their work done faster in Salesforce. Before I joined Salesforce, I was working as a contractor with an edtech company called LitLab. We had a back end interface that teachers and students could interact with. We were trying to find ways where we could use the science of reading data to help us differentiate different texts for students. Many of us struggled with reading growing up or are dyslexic, and what we’re finding in schools is that it’s really hard for teachers to differentiate text for someone at a different reading level. The team at LitLab figured out ways through prompt engineering to train ChatGPT according to different science and reading standards so that it could send us back a certain text at differentiated reading levels! There was the regular UX/UI work that I do, but I also utilized my past experience as a reading remediation therapist and to get the best information back from OpenAI or ChatGPT. This involved working with engineering teams and C-suite to ensure we had good data and the right data that we’re pushing to students, figuring out how to build trust among teachers and help them save time in their days.

Nate, you aren’t a machine learning technical expert but you work alongside the ML/AI team to integrate their work into customer integrations – How much ML/AI do you feel like you should know to work alongside these teams? 

Nate: In my experience integrating with my team at Notable, I know enough about the proprietary systems we’re using so that I can be dangerous and debug on my own without actually needing to “see what’s under the hood.” We have an excellent ML/AI team that documents their code extremely well and created these tools that enable someone who is not trained in ML/AI to use with ease. I can read the documentation, which is relatively plug-and-play, and I have a huge list of tools that enable me to work with, manipulate, and debug that part of the process without getting them involved too much. Having a knowledge of the tools you’re working with is obviously a huge plus, but my actual need and knowledge of the actual inner workings of the tools we’re using can be relatively minimal, though I’m using them all the time!

Do you need to do further upskilling or education (like a Master’s program) after the bootcamp to rise up in the tech leadership career ladder?

Lou: I didn’t feel like it was absolutely necessary, but it turns out that my company was willing to pay for it, so I did end up doing a Master’s in Computer Engineering after the bootcamp. Springboard set me up well for that because it gave me a good survey of all these different areas of machine learning, like deep learning, neural networks, things like that, that I was able to develop further expertise in in the Master’s program. When I first went into data science, I wasn’t all that interested in the theoretical underpinnings of it, but as I gained more experience I did become interested, and that’s exactly what the Master’s degree did for me. It gave me a very solid theoretical underpinning in these different areas, like writing proofs or going through the actual math of all these algorithms. It lent me more confidence in my abilities because I knew these algorithms from the ground up after that. 

Jeff, as a UX designer, did you face any difficulties getting work coming out of a bootcamp? 

Jeff: I finished my bootcamp at the end of 2018 and it took two solid months to find a position (pre-pandemic, so things were different). Looking back, it didn’t take long to find a job. I was able to network pretty heavily, which General Assembly set me up to succeed in that area. I had people I could network with from previous jobs. What was hard was being persistent in talking to connections of connections of connections and trying to find the right situation and the right place to start working in. In my current role, I mentor junior designers and I don’t see a lot of junior designers finding jobs as quickly as I did. At the time, I networked with industry professionals to talk about the industry and was then referred to someone who hired me on the spot. 

As someone working in tech now and using these new technologies, what’s your gut reaction to the headlines that insinuate AI will take away people’s jobs? 

Luke: I have good news and bad news: I think yes, a lot of jobs will be replaced by AI, but it’s going to open up more opportunities to explore! Here’s an example of what you can and can’t do with ChatGPT: If you want to create a simple, not very well designed app that does something for you, you can simply tell ChatGPT to give you the Python code to do so. It’ll give it to you and often it’ll do pretty well, but it doesn’t achieve a specific or very customized objective that you might have. In my daily job, I work with machine algorithms and usually we don’t design our new ML algorithms we apply, so we have to wrap it up into our pipeline. Very often to do that you need to write a test case. When you write a test case, you want to make sure the ML algorithm is integrated correctly into your pipeline, so you come up with a special case to see if the algorithm will output expected result, and at that point it did not. So we have to figure that out, and very often that is not the kind of thing your algorithm will tell you. ChatGPT won’t tell you the right way to fix your problem because that’s not something everybody else does -- this is very specific to what you want to do, so you have to still look into the code and figure out what’s going on behind the scenes and then make the right move. ChatGPT will help you to come up with something and that will save you a lot of time, but you have to be able to pinpoint the key of the problem.   

Nate: I agree. Those are tools I use often, and overall it does an extremely good job if you ask it to perform a specific task. But again that’s a very general sense. ChatGPT can produce code, but often there will still be small errors that I have to actually look at and think about as a human. The likelihood of a tool like ChatGPT taking my job seems low, but it is improving every day the more users use it. Overall I feel very safe in my position and in general as far as software engineering goes. 

Lou, since you’re now charged with hiring new data scientists, what are some key points people should be focusing on to be recognized in the job market?

Lou: We look for your ability to solve problems, how accurately you can identify the issue, and your ability to not only find a solution right away, but approach (finding the right people to talk to, go to the right resource and gradually put together a solution that will eventually resolve the problem). Learn SQL Appendice and if you’re proficient in that, that’s already an advantage for you to compete in the job market. 

Jeff, you have a rich career in UX and Product Design, and we got some interesting questions about your field – How would you position your portfolio to apply for UX roles at AI companies? What skills should I focus on? How is AI being used in UX / UI Design? Are there trends you’re seeing in your field? 

Jeff: It’s pretty broad, especially in design — designers are using AI in different ways and they’re designing for generative AI in different ways. There’s still a need for predictive AI — I worked with a startup that did predictive AI to visualize data in a predictive way, and communicating to users outcomes based on behaviors. What I’m looking for in people’s portfolios is ways they utilized ChatGPT to make things quicker, such as cutting down on certain processes that take a lot of time. They could show how they’ve used ChatGPT to synthesize a lot of data. For example: you have a lot of inputs and you’re trying to figure out the story you’re trying to tell and then push back on what ChatGPT offers. If you can show that you’re using those tools but also that you can think critically and push back on certain things and see things from different angles, those are skills that are highly desired in designers in our field, especially in startups or even enterprise organizations like Microsoft, Salesforce, or Apple.

Does AI foster or stifle your creativity? 

Lou: It can do both depending on how you use it. Obviously, if you’re a student and you’re using ChatGPT to write yourself essays, it’s not great. You’re not learning and you’re stifling your ability to learn, critically think, and compose essays for yourself. For me, it enhances my creativity because it lets me expand my horizons in terms of different methods and coding methodologies that I can use. Daily, I use ChatGPT to supplement my work by helping me write code which is critically useful because it allows me to not just get mired in the syntax of code and how to write it, but think more about the impact of that code and actually architecting the strategy behind my coding. Because of that, it allows me to expand my reach in terms of what I’m thinking about when coding. Additionally, it enhances my ability to not just think about syntax but also the high level architecture and impact of my code itself. 

Jeff: From a theoretical perspective and also the pragmatic side as a designer, our relationship to our tools will always be co-determetive. We are becoming something else through our interactions with our tools. Our tools are also growing in their interactions with us. This can become a flourishing relationship or it can lead to the kind of relationship where we are taking away the pieces that make us human. Creativity is ultimately an act of composition. We have learned certain skills, whether thinking critically, drawing, deciphering data, and writing code — when we offload too many of those skills on our machines, it’s a loss for us. In our technologies, always look at AI as a supplement, something that can push you a bit more as well. Our tools are growing as we grow and become something else. Don’t let the tools and machines take away what you have. Work in conjunction with it, and you’ll see creative growth. 

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About The Author

Jess Feldman

Jess Feldman

Jess Feldman is an accomplished writer and the Content Manager at Course Report, the leading platform for career changers who are exploring coding bootcamps. With a background in writing, teaching, and social media management, Jess plays a pivotal role in helping Course Report readers make informed decisions about their educational journey.

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