We are two engineers who take data science interview questions and build them into working products. We then write up the full process: what the problem was, how we solved it, and what the code looks like.
Lecturer and researcher in quantum and applied machine learning at ZHAW, and academic lead of the Innosuisse AI Booster Expert Group on Quantum Algorithms. Pavel has 9+ years in applied data science across bioinformatics, banking, network analysis, and online services. He is a PhD in Theoretical Computer Science from HSE University (Moscow). His publication record includes peer-reviewed work in computational biology journals and conference proceedings. Before moving into research and teaching, he led data science teams at BetVictor and Sberbank.
Software engineer with 10+ years of experience building data-intensive applications in Java, Kotlin, and Python. Sergey currently designs backend systems at Plata Card (fintech). He previously built microservices at Arrival, the British EV startup, and spent four years at HeadHunter (hh.ru), Russia's largest job platform, where he led the ads service that became the company's second-largest revenue source. He holds a Master's in Mechatronics from Bauman Moscow State Technical University and has published research on control systems.
Most data science interview questions are based on real problems that companies face. The hard part is usually not the algorithm but getting it to work reliably in production.
We take these problems, build actual products around them, and write up what happened. Each write-up follows the same five sections: Problem, Challenge, Solution, Code, Summary.
Pavel handles the data science, ML, and quantum computing side. Sergey handles the backend systems and infrastructure. Between the two of us, we cover the path from prototype to deployment.
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