A school math teacher refers to a dry textbook in response to a simple question, "What is a logarithm?" A familiar plot to many, which for some becomes an excuse to close the topic of exact sciences forever, and for others turns into an obsessive, open-ended gestalt for years to come.
A school math teacher refers to a dry textbook in response to a simple question, "What is a logarithm?" A familiar plot to many, which for some becomes an excuse to close the topic of exact sciences forever, and for others turns into an obsessive, open-ended gestalt for years to come.
Vitaliy Oskalenko chose the second path. Having gone from Kherson Polytechnic and his first job at Ukrtelecom to the positions of team leader and CTO of the mobile division at Wezom, and now — Android team leader at Duvon Corporation, he could have just calmly developed marketplaces. However, the desire to understand the fundamental origins of technologies has not disappeared, especially now — in the era of the generative AI boom, when engineering work is undergoing tectonic shifts.
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 create his own product.
For Vitaliy, a career in development has always been closely intertwined with an attempt to get to the heart of things. Even during his first steps in IT, observing his old-school colleagues, he felt the difference between superficial coding and real engineering school:
"I was still at the first company, I remember, programmers from the Odessa branch were supposed to come to us. That was the first time I saw such smart programmers — with lenses, in such sweaters :). You can be a programmer without knowing mathematics, especially now. But this is not my option. I always wanted to know the origins, where it all came from."
Having received master’s degrees in economic cybernetics and finance in addition to programming, Vitaliy spent a long time building mobile products. However, the feeling that the industry was moving towards automation and templates only strengthened his desire to return to deep knowledge.
When social media was flooded with endless Data Science courses, Vitaliy decided that the time had come. The main criterion was not marketing or promises of “entering IT in two months”, but the presence of academic depth and higher mathematics. So the choice fell on the Vodafone Big Data Lab school — almost the only program in Ukraine where the telecom giant’s business cases are supported by a serious university approach.
The immersion in a new direction began somewhat spontaneously. The first lesson of the preparatory mathematics module found Vitaliy on the road.
"I remember the first lesson very well. I was not at home, so I had to connect from my phone. I expected it to be another standard theory lecture: you can turn off the video, listen in the background, and not strain yourself 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 on your smartphone, not at your workplace. I had to urgently remember everything I’d forgotten."
This cold shower quickly made it clear: there would be no easy surfing of the material here. The lectures lasted three hours and required maximum mental concentration. Vitaliy recalls that he often reviewed the class notes the next day to work out the formulas down to the last symbol.
The atmosphere in the group and the presentation of the teachers were the lifeline. The academic lecturers of the course, including Professors Oleg Chertov and Andriy Stavytsky, managed to combine complex concepts with humanity and subtle humor.
"Siting on such material for three hours is really stressful. If it were presented dryly, it would be incredibly difficult. But the subtle humor and dedication of the teachers made the process fascinating. In addition, the group gathered strong specialists from different fields, real intellectuals. We supported each other, analyzed complex cases, and joked a lot. This created a cool home atmosphere that saved us from burnout."
What impressed Vitaliy the most about his training was the absence of the popular infogypsy format of “learn 10 functions of the Pandas library and call yourself a data scientist.” The focus was on the essence of the processes:
"The school is not limited to familiarity with Python or ready-made tools. It provides the fundamental mathematical foundation necessary for a true understanding of the discipline. When you see what is happening "under the hood", algorithms cease to seem like magic."
Another important detail is working with real depersonalized Vodafone data and direct contact with the telecom operator’s specialists. Students did not just calculate abstract matrices, but investigated how big data helps businesses open retail outlets or optimize their network.
"It felt not just like training, but like an internship at Vodafone. You see real algorithms, you see the logic of people’s behavior. Both psychology and business thinking come into play here — you understand why users act in a certain way and not otherwise."
The final project required full dedication not only from the students, but also from the mentors. The number of iterations and reviews was so intense that Vitaliy’s financial background made him smile and reflect on the economics of the course:
"I’m a financier by education and I calculate everything. When we handed in the final project, there were so many checks and feedback that I thought: how much more can we do, they’re already working in the red! Compared to other courses, where one speaker tells general things for hundreds of thousands of hryvnias, this looked more like altruism and an investment in community development."
The knowledge he gained didn’t just sit there as a dead weight in his notes. Vitaliy immediately began converting it into his own pet project — a platform for learning and personal development using artificial intelligence.
The project was conceived as an intelligent harvester and digital mentor that not only answers questions, but also guides the user through their goals, remembers plans, and acts as a kind of internal compass. Thanks to his understanding of Data Science principles and deep learning methods, Vitaliy was able to effectively integrate complex AI models and recommendation approaches into it.
The service is currently in closed beta testing among friends. What’s more, the platform was created using the latest spec-driven development practices and code generation assistants:
"If I were to write such a large system using the old methods, it would take three to five years. Tools like Anthropic’s Claude are now capable of writing code better than the average person, if properly managed through systems engineering. But to properly formulate the problem and design the architecture, you have to be an expert yourself."
Recently, Vitaliy, for the sake of sports interest, took part in a global selection of one technology company for the Agentic Engineering program: out of 1250 applicants, he was included in the list of the 88 best. This experience confirmed his hypothesis about the future of the labor market: companies are increasingly looking for developers who simply translate tasks into code. What is being replaced is a demand for universal engineers who combine the expertise of a product designer, architect, analyst, and business thinker.
When asked whether he thinks studying complex theory is a waste of time in an era when generative models can automate most tasks, Vitaliy answers categorically. In his opinion, it is the total development of AI that makes mathematics a vital necessity:
"Now artificial intelligence is taking over more and more routine, and there is a great temptation to stop thinking. If a person stops solving complex tasks, his critical and analytical thinking degrades. AI should be an assistant, a navigator, a companion, but in no case a genie to whom you give control."
Vitaliy is convinced that preserving a strong engineering culture in Ukraine directly depends on restoring respect for exact disciplines — both in classical education and in corporate initiatives.
"The future of Ukraine 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 the Big Data Lab was not just a study for the sake of a line in my resume, but a real intellectual marathon that shook my brain and reminded me: the main advantage of a person is the ability to think independently."
