A high school math teacher sends you off to a dry textbook in response to a simple question: “What is a logarithm?” It’s a familiar plot—one that for some becomes a reason to close the book on the exact sciences forever, while for others it turns into an obsessive unfinished gestalt for years to come.
Vitalii Oskalenko chose the second path. Having traveled from Kherson Polytechnic and his first job at Ukrtelecom to team lead and CTO of the mobile division at Wezom, and now Android team lead at Duvon Corporation, he could have simply continued calmly developing marketplaces. But the desire to understand the fundamental origins of technology never disappeared—especially now, in the era of the generative AI boom, when engineering work is undergoing tectonic shifts.
This is the story of why an experienced mobile developer needed to solve school problems from his phone, why Data Science is not about Python libraries, and how the synthesis of mathematics and artificial intelligence helped him create his own product.
The Unfinished Gestalt and Sweaters with Glasses
Vitalii’s career in development has always been closely intertwined with an attempt to get to the essence of things. Even during his first steps in IT, watching old-school colleagues, he felt the difference between superficial coding and a genuine engineering school:
“I was still at my first company, I remember, programmers from the Odesa branch were supposed to come to us. That was when I first saw true programmers—with glasses, in sweaters like that :). You can be a programmer without knowing mathematics, especially now. But that’s not really my option. I always wanted to know the origins, where it all came from.”
In addition to programming, he also earned master’s degrees in economic cybernetics and finance, and for a long time Vitalii built mobile products. But the feeling that the industry was moving toward automation and templates only strengthened his desire to return to deeper knowledge.
When social media began overflowing with endless Data Science courses, Vitalii decided the time had come. The main criterion was not marketing or promises to “get into IT in two months,” but the presence of academic depth and higher mathematics. So his choice fell on the Vodafone Big Data Lab school—perhaps the only program in Ukraine where the telecom giant’s business cases are backed by a serious university approach.
Shock at the First Lecture: When You Join to Listen in the Background and End Up Having to Do Math
The immersion into the new field began somewhat spontaneously. The first class of the preparatory mathematics module caught Vitalii on the road.
“I remember the first class very well. I wasn’t at home, so I had to join from my phone. I expected it would be another standard theory lecture: you can turn off the video, listen in the background, and not stress too much. But I was very wrong. From the very first minutes, we started solving math problems online. At first it was a shock! Especially when you’re sitting with a smartphone rather than at a desk. I had to urgently recall everything I’d forgotten.”
This cold shower quickly made it clear: there would be no easy surfing across the surface of the material here. Lectures lasted three hours and demanded maximum mental concentration. Vitalii recalls often watching recordings of the classes the next day to work through the formulas down to the last symbol.
A lifeline was the atmosphere in the group and the way the instructors presented the material. The course’s academic lecturers, including professors Oleh Chertov and Andrii Stavytskyi, managed to combine complex concepts with humanity and subtle humor.
“Sitting through three hours of material like that is genuinely demanding. If it were presented dryly, it would be incredibly hard. But the lecturers’ subtle humor and dedication made the process fascinating. On top of that, the group brought together strong specialists from different fields, real intellectuals. We supported each other, worked through difficult cases, and joked a lot. It created a great homely atmosphere that saved us from burnout.”
Not Just Python: A Look Under the Hood of Telecom
What impressed Vitalii most about the training was the absence of the popular infoguru format of “learn 10 functions of the Pandas library and call yourself a data scientist.” The focus remained on the essence of the processes:
“The school doesn’t limit itself to an introduction to Python or ready-made tools. It provides the fundamental mathematical foundation needed for a genuine understanding of the discipline. When you see what is happening ‘under the hood,’ algorithms stop seeming like magic.”
Another important detail was working with real anonymized Vodafone data and direct contact with specialists from the telecom operator. Students didn’t just calculate abstract matrices; they explored how big data helps a business decide where to open retail outlets or optimize its network.
“It felt not just like studying but like an internship at Vodafone. You see real algorithms, you see the logic behind people’s behavior. Both psychology and business thinking come into play—you understand why users act one way and not another.”
The final project demanded complete dedication not only from the students but also from the mentors. The number of iterations and reviews turned out to be so intensive that Vitalii’s finance background made him smile and wonder about the economics of the course:
“I’m a finance major by education and I calculate everything. When we were submitting the final project, there were so many checks and feedback that I thought: how much longer can they keep working with us—they’re operating at a loss! Compared with other courses where, for hundreds of thousands of hryvnias, one speaker retells general things, this looked more like altruism and an investment in developing the community.”
His Own Startup and Agentic Engineering Technologies
The knowledge he gained did not settle as dead weight in his notes. Vitalii immediately began converting it into his own pet project—a platform for learning and personal development with the help of artificial intelligence.
The project was conceived as an intelligent all-in-one system and digital mentor that doesn’t just answer questions but guides the user through their goals, remembers plans, and serves as a kind of internal compass. Thanks in part to his understanding of Data Science principles and deep learning methods, Vitalii was able to effectively integrate complex AI models and recommendation approaches into it.
Currently, the service is in a closed beta testing stage among friends. Moreover, the platform was created using the most modern practices of spec-driven development and code-generation assistants:
“If I were writing such a large system using old approaches, it would take three or five years. Tools like Claude from Anthropic are now capable of writing code better than an average mid-level developer, if you manage them correctly through systems engineering. But to formulate tasks correctly and design architecture, you yourself have to be an expert.”
Recently, for the fun of the challenge, Vitalii took part in a global selection process by a technology company for an Agentic Engineering program: out of 1,250 applicants, he made it into the list of the top 88. This experience confirmed his hypothesis about the future labor market: companies are increasingly less interested in simply developers who translate tasks into code. Instead, demand is growing for versatile engineers who combine the expertise of a product owner, architect, analyst, and business thinker.
Why Mathematics Matters More Than Ever
When asked whether studying complex theory doesn’t seem like a waste of time in an era when generative models can automate most tasks, Vitalii answers categorically. In his opinion, it is precisely the total development of AI that makes mathematics a vital necessity:
“Right now artificial intelligence is taking on more and more routine work, and there is a great temptation to stop thinking. If a person stops solving complex problems, their critical and analytical thinking degrades. AI should be an assistant, a navigator, a companion, but in no case a genie to whom you hand over control.”
Vitalii is convinced that preserving a strong engineering culture in Ukraine depends directly on restoring respect for the exact sciences—both in classical education and in corporate initiatives.
“Ukraine’s future rests on engineering, high tech, and the ability to create our own products. We need specialists who look deeper than the interface. The course at Big Data Lab became for me not just training for a line on my résumé, but a genuine intellectual marathon that shook up my brain and reminded me: a person’s main advantage is the ability to think independently.”