How A Passion for Problem Solving Opened the Door to a Career in Data Science

Kayode Makinde didn’t always have a clear plan when it came to his career in tech. Initially aiming for something different, an event he attended changed his direction and led him to discover a passion for data science. What began as a shift in plans soon became the foundation for his future. Early in his journey, he focused on getting real-world experience rather than just chasing grades. For him, this helped him learn things that were far more valuable than what you might find in a textbook.

One thing Kayode wishes he had known earlier was the importance of networking and building relationships. While it’s easy to get caught up in the hustle for money and recognition, he found that some of the best opportunities didn’t have an immediate financial payoff. He’s learnt to value social capital just as much as technical know-how, and his story is full of lessons for anyone trying to figure out how to stand out in the tech space.

In this interview, as part of our AMTES Alumni Series, Kayode reflects on what he wishes he had known earlier, the importance of doing your best in school, and why experience outside the classroom can shape your future just as much as the grades you graduate with. His story is a powerful reminder that success is built not just on knowledge but on the experiences that shape you along the way.

Hi, Kayode! Welcome. Thank you for being here and agreeing to do this with me. It really means a lot. It’s a pleasure to have you. We have about 10 questions for this interview. For our first question, can you walk us through your journey from studying mechatronics to getting involved in data science? What made you interested in AI and data science?

I’ve always loved mathematics. It’s something that came naturally to me, even from a young age. That love really grew stronger when I got to secondary school. In senior secondary, especially, I took courses like physics, chemistry, math, and economics, subjects that were closely linked in some way. I started to realize that I wasn’t just good at learning the concepts, I was also good at applying them. And I enjoyed that a lot more. I liked the idea of using what I’d learnt to solve real problems.

When it was time to decide on a university course, around 2017/2018, my exposure to computer science was honestly very limited. At the time, computer science to me was just word processing, Microsoft Office, Excel, that sort of thing. I didn’t find it exciting, so I ruled it out pretty quickly. Since I was strong in math and calculations, most people around me suggested engineering. And that made sense to me, too. It felt like the one field where I could use what I knew to actually build things.

I got into FUNAAB and chose mechatronics, but it wasn’t exactly what I imagined. I thought it’d be a lot more hands-on. During my first year, I attended a Data Science Nigeria (DSN) event on an introduction to data science and machine learning. That was my first real exposure, and it completely fascinated me. I figured since we might eventually cover some of that in school, I’d get ahead and learn it early so it wouldn’t catch me off guard later.

But more than that, I was thinking long-term. I didn’t want to graduate and then start figuring things out from scratch. I wanted to be job-ready, to have skills that would open doors after school. University takes so much effort and sacrifice, so it didn’t make sense to me to go through all that and then have nothing going on afterwards. So that’s how I found myself diving into data science. Does that answer your question?

Yes, yes, it does. I actually have a similar story. Back in senior secondary school, I just found myself really enjoying physics. It just clicked with me, and I wanted to keep learning more. It made me see things differently, in a good way, and I remember thinking, “OK, I think I really like this.”

At first, I actually wanted to study physics at university. But then I started seeing things like, “Oh, to really have a solid career in physics, you’d probably need to go all the way and get a PhD.” And honestly, that felt a bit too adventurous for me at the time. So I thought, why not go for something that still involves physics but is more practical and applied? That’s how I ended up choosing engineering, and I eventually settled on mechatronics.

You’ve kind of already touched on what we were going to ask next, which was why mechatronics? Was it something you always wanted to do? But if there’s anything else you’d like to add, feel free to shed more light on it, or we can just go ahead to the next question.

Yeah, I can shed more light on that. Like I mentioned earlier, I chose mechatronics because it felt like a way for me to apply what I knew in a practical way. I’ve always liked learning things I can actually use, and mechatronics gave me that.

It also gave me a way to explore my interest in machine learning through data science. I could see real, tangible applications of what I was learning. It felt like I could actually build stuff with it, solve problems, and make things work. That hands-on aspect really drew me in.

And because data science and machine learning are closely related to mechatronics, I didn’t feel like I was abandoning my degree for something completely different. I was just leaning more into the software side of things. So yeah, I basically dialled down on the part of mechatronics I wanted to focus on for now. Tomorrow, I might explore a different side of it; who knows?

I think my final year project really brought everything together. It was AI-based, and the knowledge I had gained from machine learning played a huge role in making that project a success.

Thank you so much for that. It’s something I really resonate with because one of the reasons I’m still pursuing a career in data science is that it ties in so well with mechatronics. So it’s not like I’m studying two separate things or dropping one for the other.

But yeah, moving on. You’ve won two hackathons and received two AI awards, which is very, very impressive. How did those experiences shape your growth in the data science field? Is there any backstory leading up to those achievements that you’d like to share?

That’s a very, very good question. Last year alone, I won 2 hackathons and 2 AI awards at AI research conferences.

I’m sending virtual claps, by the way.

Haha. Many people have called me a serial hackathon winner, but the truth is, before I became that, I was a serial hackathon ‘loser’. I’ve participated in over 30 hackathons since 2020 (you can check my Zindi profile), and while I’ve ranked high in a couple, like top five or top two, it wasn’t always like that. That consistent participation and the knowledge I gained along the way really helped contribute to the wins I had last year. Maybe being out of school at the time also played a part.

Now, when it comes to how those wins have shaped my career, funny enough, the biggest win hasn’t been the money. And I feel like I’ve earned the right to say that because I’ve won a few that were quite high-paying. But honestly, the real value has been the social capital. It’s the connections, the reach, the visibility. It’s the proof of competence. These wins have been a big part of my grad school applications, and they’ve definitely helped with that. They’ve also been a major confidence boost.

And even financially, most of the money I’ve made didn’t come directly from the hackathons, it came from the opportunities that came after. Sharing those wins put me on the radar of people at the top of their fields, people who then reached out to work with me and were willing to pay reasonably well.

So yeah, I’d say these wins have shown me what’s possible, not just for me, but for others too. It’s helped me grow, and it’s also made me want to help and inspire others to do the same. Does this make sense?

Yes, it really does, especially your last statement. I can imagine how inspiring it must have felt. Being there to encourage people and tell them they can achieve the same things. I can only imagine the amount of growth you’ve had since that experience. Congratulations once more. I feel I should keep on saying that throughout this interview. But you mentioned Zindi during your last response, and our fourth question is related to that. Competing in the data science space is very tough. How did you develop your skills to navigate and climb the ranks to the 0.1 per cent on Zindi?

I’m not sure how you got that number, but yes, it is accurate.

Haha, I did my due diligence.

I was top 25 globally on Zindi in 2022 or 2023. One thing that helps with hackathons is good team members, and they come from you doing well in other hackathons. It’s like a chain effect. The people I teamed up with the most were people that, when I was at the top in certain experiments in different hackathons, reached out to partner with me. That was one thing that helped. These people were already good, but they were struggling in areas where I excelled, and we teamed up to learn from each other. Going forward, we participated in some competitions together, and I was able to rank high.

Another thing is research. Make your findings about the things you’re working on. Nothing is entirely new, especially in this age and time. So when you’re working on a problem for a hackathon, you want to look at Kaggle, GitHub, and even research papers on that topic. It gives you that domain knowledge, which is really as important as having the technical skill. Say it’s a healthcare-related hackathon. You want to learn from medical professionals, know all the things to look out for. I think a mix of all those things really helped my hackathon career.

I feel like what you gave there was a beautiful piece of advice that maybe not a lot of people think or know about. A lot of our readers would definitely learn from this one response alone. I didn’t send the questions beforehand, but the flow of your responses really ties into the flow of my questions. The next question here, our fifth one, is about research, and you just mentioned that. Research is a big part of what you do. How did you get started, and what has been your most exciting project so far?

I got started in machine learning research through ML Collective. I don’t know if you know them. It’s a research community with a subgroup for researchers in the Nigerian space headed by some of our past FUNAAB gurus like Steven Kolawole, Busayo Awobade and, Mardiyyah Oduwole. ML Collective really helped me in my research career. I didn’t even know what ML research was before them. ML Collective, especially the Nigerian arm, really simplifies research and shows that anybody can do research.

The most interesting project I’ve worked on so far, which was my first actual research project, was a reinforcement learning agent playing Ayọ. For those who don’t know, Ayọ is a traditional Nigerian strategy game that has a lot of possible moves and requires a lot of computation. I built reinforcement learning agents to play the game, which was really interesting to me as I got to apply my knowledge to solve a problem, something I had always wanted to do.

Beyond the personal satisfaction I got from the project, one thing that makes me happy about the project is the recognition it has gotten, both locally and internationally. You’ll get more details on that later. But more than anything, the project shows that with the skills we already have, we can solve problems around us, no matter how little our knowledge might seem at the time. One thing I’ve noticed is that, in Africa, we don’t have enough experienced talent, or large datasets, or even enough computing power. But what we do have is a rich culture and a strong understanding of our problems. That alone can help us build solutions that are meaningful and relevant to our communities. And that cultural relevance is just as important as the technology itself. The project basically shows that. I believe this is a good answer.

It’s a very well-said and beautiful answer; it’s more than good. You mentioned ML Collective, and yes, I know of the community and the amazing things they’ve been doing for African researchers. And what you said about us having cultural context and being in the best place to solve our problems was one of the things that really drew me to research. I think it was a session I attended or a friend who shared this with me, about how there are problems in Africa, and we have to be the ones to solve our own problems. Globally, there are a lot of challenges, and we can’t always expect someone solving a problem in their own country to come solve our problems for us. We have to be the ones to stand up and solve the issues we face. That drew me to research, and you also spoke about that, which is very nice.

To add to that, I had an experience last month that really solidified that for me. I was reading a research paper about a traditional game that’s native to Ghana, and interestingly, the researcher behind it was American. While going through it, I noticed a pretty significant error, one that any local player would’ve easily caught. I don’t blame the researcher; he had simply misinterpreted another paper he had referenced. But that’s exactly the point: context matters.

In this case, the error was in the domain of games, so it wasn’t a big deal. But what if it had been in something like healthcare or any field where the consequences could be life-threatening? It just shows how important it is for us as Africans to build Africa-driven solutions. We understand our own realities, and that perspective is something no amount of data can replace.

That was a really eye-opening addition because, as you said, if it was in a more life-threatening domain, the effect would have been bigger. Now, we’re going to leave the tech and research space and move back to academics. What was your academic journey like? Were there moments where you struggled, and how did you push through?

Haha, I think I’m a master of academic struggling.

Seconded!

Don’t worry. When I give my answer, you’ll know it’s on different levels.

From secondary school, I was quite a bright academic student. I got into FUNAAB as the best-scoring student in JAMB. My 100 level in FUNAAB was not bad. I think I finished with a CGPA between 4.0 and 4.3. 200 level was also fairly okay. Everything started going downhill in 300 level.

This was when it hit me that we wouldn’t actually do anything hands-on. The level of abstractness of our education system became clear to me. In 100 level, I felt it was because it was just an introduction and a revision of the things we did in secondary school. I saw 200 level as an introduction to engineering. It started to hit me in 300 level that I wasn’t getting what I came in expecting. It wasn’t what I wanted.

That thought, paired with my earlier bad habit of gaming too much, discouraged me from attending classes. There were some classes I didn’t attend till their exams. The lecturers would see me and ask if I was a student. There were some classes I only attended for the test because I was aware of them, and that really affected my academics. It really brought me down. I think my GPA that semester, 300 level first semester, was less than 2. You can see how bad that was.

To recover from it, a really important support system I had was my family. I spoke to them before things got too bad. I even considered dropping out. What stopped me was the question my dad asked me. He didn’t ask me not to drop out; he didn’t complain; he just asked, ‘What next?’. I did not have a genuine answer to that question. I was already making a bit of progress in the AI and ML space at the time, but to make even more progress, I still needed a degree. That question changed my perspective. It made me realise I was pursuing a degree for the greater good, for my future, and to open doors to what I love doing.

In my subsequent semesters, I was able to catch up and finish with a not-so-bad result. I believe a second-class upper in engineering is not a bad result anywhere. That’s it about my academic struggles.

I feel like with every response, I’ve just been genuinely honoured to have this interview with you. Each answer has been so unique, thoughtful, and beautiful. I keep finding myself inspired all over again, and I just want to say thank you once more for choosing to do this with me. I’m not saying that out of formality; I truly mean it.

What you said about having a good support system really struck a chord with me. I’ve experienced that personally. There have been times when I felt completely weighed down by everything going on, and just having people I could reach out to, people who would listen and help put things back into perspective, made all the difference. It has honestly helped me a lot.

Yes, it really helps. One other thing I’d like to add, maybe other questions might lead to it, is also your relationship with God. It gives a sense of purpose and relaxation. You just know that everything is going to be fine. He’s holding your hand, and he’ll see you through whatever you’re going through. That faith is really important.

Exactly. One faith-defining moment for me was when I was trying to gain admission into the university. I had an experience that made me see this sense of purpose and relaxation you mentioned. Highly recommend.

But yeah, moving on to our next question. It’s our seventh one, so we have a little more to go. How did you balance your academics with everything you had going on without feeling overwhelmed?

I don’t remember feeling very overwhelmed; it’s not a feeling I have to deal with very often. I understand that balancing school with a tech career is one of the hardest things one could ever do. I didn’t do it exceedingly well, but I did my best. Now that I’m done with school, I’ve gotten more insight into it and advice from people who were able to do it exceedingly well, so I’ll just share those.

The truth is, tech is always going to feel more exciting. It’s fast-paced, it looks like the future, and there’s this constant pressure that if you don’t jump in now, you’ll miss out forever. I felt the same way too, especially in 100 level when I didn’t have access to a laptop. I used to feel like I was already behind before I even started. That feeling makes a lot of students shift their focus more to tech and less to school.

My advice? As much as you want to do both, if you ever find yourself in a situation where you have to choose between tech and school, choose school. It’s not always that black and white, though. For example, if you have a chance to present a paper at an international conference, go for it. I don’t care how many classes I have that week, I’d go. But if it’s a situation where you’re unsure, especially when tests or exams are involved, pick school.

You already know the classes that carry weight. Those ones where attendance matters or where the course itself needs extra time. You need to build a solid academic foundation first. But that doesn’t mean neglecting tech either. For me, the COVID-19 lockdown and the ASUU strike really gave me time to focus on learning tech.

If you want to focus on both, you’ll probably have to let go of other things that eat up your time. It’s not easy, but in the words of a friend, “You either sacrifice for what you want or what you want becomes the sacrifice.” You might have to cut back on social life a bit. Time management will be your best friend.

There’s one last thing I’ll share. It’s a tip I got from someone who balanced both and did really well. He also studied engineering at FUNAAB. What worked for him in his later years, like his fourth or fifth year, was using the first two hours of his day strictly for academics, even before classes. Then, during class, breaks or after, he’d work on his tech stuff. It didn’t seem like much at the time, but those little pockets of consistency added up.

At the end of the day, your academics are the bedrock for everything that comes next, especially if you’re planning to go into AI research. Degrees really matter in that space. It’s different from other tech fields where you can get by with a portfolio. Most people only get one shot at undergrad, and that result can carry you far. I’m not an expert on the topic, but that’s my take.

You really did justice to that question. Thank you. This is question eight. Many people want to get into AI, machine learning, engineering, or research, but they don’t know where to start. What advice would you give them?

Hmmm, that’s a good question. It’s been a while since I was a beginner, and the field has changed a lot since then. If you’d asked me this question 2 or 3 years ago, my answer would have been more direct.

But first, you’d need to learn a programming language, and I believe the best one to learn is Python. The most popular programming language in the data science space is Python. Don’t be like this guy, my rival, Ghostmac, who went to learn Rust. Don’t complicate things for yourself.

Next, learn the basics of machine learning. I would recommend this YouTuber, Krish Naik. He also has this machine learning playlist and a lot of other helpful playlists. Kaggle is also a nice resource. They have a lot of free short courses you can take. I’d also recommend Datacamp, a platform you can learn from, and there are a lot of scholarships for this around.

All these are just for the introduction. Please note that no matter how rich these videos and courses are, they will never sufficiently prepare you for the real world. You need to actually build projects. So as early as possible, as you’re learning, have it in mind that you have to apply what you’re learning. If you’re learning computer vision, you need to start looking for computer vision-related problems around you. You don’t need to have completed the course materials, which brings me to my next point. You don’t need to complete a course material to start applying what you learn. The purpose of knowledge is not just for itself but for its application and the solutions it can bring forth.

So yes, I would suggest learning Python, the basics of machine learning, and learning through building by participating in hackathons and maybe through machine learning research.

The final thing I’d love to add is being part of a community. Try to find a community that supports your goals. These are some examples on campus:

• GDG FUNAAB,
• DSN FUNAAB.

There are also other communities like

• ML Collective,
• Data Community Africa. There are a lot of communities doing amazing stuff, and they provide the platform for you to achieve your goals. They won’t necessarily always push you or carry you on top of their heads, but they’ll help you and give you the opportunities you need and help you define what you want. You can find mentors in these communities as well. But yeah, that’s my advice. I tried to keep it pretty generic as the field and resources change over time.

You’ve said everything, and you’ve said it in the best way. The next question is, looking back, is there anything you wish you’d have known earlier that would have helped you throughout your journey in tech?

Something I was told but wish I had done earlier was to try to increase my social capital. Many times, in our hustle, the main goal is an immediate monetary gain. I did it a bit; it’s not bad, but I wish I knew better. There are some opportunities without monetary value, but they would really help your career. For example, applying for fellowships, attending conferences and events in your field, and doing projects that don’t pay.

I did a little bit of ghost work in my career, where I did stuff for other people but am unable to add my name to it, talk about it, or add it to my resume. I wish I did that a bit less. During my early Zindi competition days, I wish I didn’t just compete but also did projects on the side too. I would have learnt more.

One thing I wish I had done was get myself into the industry earlier. While I was in school, I could have gotten some internship roles, not just for the money but for the experience and the year count.

Let me explain. If I’m asked how many years of experience I have as a data scientist, I would say 4 to 5. But my professional work experience shows otherwise. My actual first full-time role was in January last year. I’ve had a couple of internships before then, but that makes it about two years of experience. My knowledge and experience is beyond what I can put on paper, and paper is what is seen first before you’re invited for an interview. While chasing things that will give you the experience and knowledge, you need to also pursue things that will make your resume stronger.

I also wish I took my academics more seriously.

Your last statement actually leads us into our final question, which is, what is one lesson from your academic journey that you think every student should know? I feel you’ve already answered that unless you want to shed more light on it.

I’ll just wrap everything up with this. You’ve gotten admission, and you’re in school. Just for the fact that you’ve even come this far, you need to do your best. Anything that is worth doing at all is worth doing well. Do your best to finish with the best grade possible.

Next, don’t finish school without any experience. If you’re business-orientated, start a business while in school. If you’re a career person, start your career while in school.

You also want to prepare for life after school while in school. You shouldn’t go to university because it’s the next step after secondary school or what everybody is doing. Define what you want to do after school and start channelling your efforts towards achieving that from 100 level. Nothing will miraculously change after you graduate. If you weren’t close to getting a job before finishing school, you won’t just get a job right after.

Academics and education are very important. Your academic standing is very important. But what you can do with the knowledge you have is even more important. In my current company, where I work with them as a consultant, they’ve not even seen my CV. During my induction, I was having a call with them, and this person asked, “What did you study again?” He didn’t even know whether I completed school or not nor the grade I finished with.

Beyond your grades, you need to bring something practical to the table. Coupled with your already excellent academic background, that would make you almost unstoppable. It puts you in a very good place for your future.

What a way to finish things off. I feel like everything you said here should be immortalised in stone or written on the walls of the department. I just found myself learning a lot and being in awe. Thank you for sharing your story and giving me your time, even on your birthday. I’ll work on getting this blog out soon because I believe everybody should read this ASAP.

The pleasure is mine. Thank you for giving me the chance to share my little knowledge.

Little?! The pleasure is even more ours.

Thank you, and thank you, AMTES; this is a good initiative. I wish I had had this as a student.

Right! Because I’m already thinking about the younger students that would see what we’re putting out and how it’ll guide them and inspire them, so thank you.

You’re welcome, you’re welcome.

I hope you have a lovely rest of your day. Bye!

Kayode’s story shows that you don’t always need to have everything figured out from the start. Sometimes, just being curious and taking one step at a time is enough. His love for math led him to data science, and even when school felt overwhelming, he didn’t give up. A simple question from his dad, “What next?” helped him see the bigger picture.

When Kayode taught AI to play Ayọ, he showed that our culture can be part of how we solve problems. He believes that we already understand many of the issues in Africa and that using what we know can help us create better solutions. He believes that the problems we see around us and the way we understand them are important in building solutions that truly make a difference.

From struggling to win hackathons to now winning awards, Kayode’s journey is built on patience, grit, and quiet courage. His story is about showing up again and again. And for every student still figuring things out, Kayode’s path offers hope that, with consistency and curiosity, you can build something truly meaningful.

From all of us at AMTES, we celebrate you!

You can connect with him on LinkedIn.