In this edition of After Hours, Gift Ojeabulu shares how an unplanned switch to computer science, a crashed laptop, and a growing curiosity for problem-solving led him into machine learning.
What began as a childhood fascination with computers in Lagos cyber cafés has evolved into a career in machine learning, data science, and MLOps. And while he initially wanted to become a medical doctor, Gift Ojeabulu found his way back to tech, with a few stops along the way as a rapper and dancer. Today, he is a machine learning engineer working with global teams and a co-founder of Data Community Africa, an initiative advancing data and AI education across the continent. In this edition of After Hours, Ojeabulu walks us through his journey from predicting Champions League scores with data models to becoming a machine learning engineer despite Early encounters with technology My earliest memory of technology takes me back to primary school. I grew up in Ketu, Lagos, when cyber cafés were everywhere. For many people, those cafés were tied to a certain kind of Internet culture, but for me, it was a window into something fascinating. Through my older brothers, I was exposed to computers earlier than most of my peers. Whenever I got money for school, I would sometimes sneak into a café to browse for a few minutes. That was my first real interaction with a computer, mostly clicking around and learning to control the mouse. Even in school, I stood out during ICT classes. At the time, I didn’t fully understand what that meant. I just knew I enjoyed it. But if I’m being honest, gaming got to me way before that. We had a Sega at home, and I was always on it. My brother and I would also head to the game shop to play Winning 11, PS1 and PS2. Those were good times. By SS1, I had my own laptop, though I mostly used it for FIFA and Mortal Kombat. Around that same time, I was also DJing and playing at friends’ parties and birthdays. I didn’t think of it as tech then, but I was always fiddling with equipment, figuring things out. I was also a big fan of sci-fi movies. The Matrix and Blade were the ones that really stuck with me. Looking back, all of it was quietly shaping how I saw technology; I didn’t realise it at the time. From medicine to machine learning My academic journey didn’t start with tech in mind. I originally wanted to study medicine and surgery, like many Nigerian students. When I didn’t hit the cut-off mark, I switched to medical biochemistry. Eventually, due to academic requirements, I was moved to computer science at Ambrose Alli University. Victoria Fakiya – Senior Writer Techpoint Digest Stop struggling to find your tech career path Discover in-demand tech skills and build a standout portfolio in this FREE 5-day email course It wasn’t planned. In fact, when I got into computer science in my second year, I had to ask myself, “Where do I even begin?” The answer came through curiosity. I’ve always been a multi-faceted person. I rapped, I danced, I played the drums, and even acted. At the time, I had a friend who was already building with web technologies like HTML and Bootstrap. I would follow him around, watch him work, and observe how he turned lines of code into actual products. Soon, I started learning on my own, with Udemy, Udacity, and anything I could find. I explored web development, then transitioned into Android development, diving deep into Java. I was fascinated by the idea of building something from scratch. Around 2017–2018, I built my first real project, an Android app. It wasn’t deployed, and I didn’t even know about GitHub at the time, but it worked. My friends and I could use it. Then my laptop crashed. Everything I had built was gone. At that point, I didn’t have the money to replace it, so I stepped back from tech temporarily and focused on school. Looking back, that moment could have ended my journey, but thankfully, it didn’t. In 2018, a friend introduced me to an AI bootcamp. That was my entry point into data science and machine learning. I started learning online, watching lectures, studying concepts like eigenvectors, and diving into the fundamentals. By 2019, I participated in a competition and ranked among the top participants. My team also won the Best AI Poster award at a national event in Lagos. That experience made things clear for me: this was the path I wanted to pursue. From there, I doubled down. In 2020, I took more structured courses, including programs that helped me understand not just the technical side, but also the business side of AI. By 2021, I landed my first major role as a data scientist at a basketball analytics company, where I applied machine learning to sports data. That role opened doors. I later worked as an MLOps developer advocate at a global AI company, where I spent about two years. At one point, another company approached me with an offer and asked me to ...
