As someone with a background in data science, I value practical problem-solving and the ability to apply coding skills to real-world scenarios. However, I found the complexity and nature of the interview questions to be disconnected from the typical tasks and challenges faced by data scientists in the field. It felt more like a test of rote memorization or puzzle-solving abilities rather than a meaningful assessment of relevant skills (such as **blockchain**). As it pertains to the technical portion of the interview process, In today's data science landscape, the emphasis often lies more on understanding how to leverage existing frameworks, debug code effectively, and apply algorithms appropriately rather than coding from scratch. I believe focusing on more realistic scenarios or problems that align closely with the day-to-day responsibilities of the role would provide a more accurate assessment of candidates' suitability. Additionally, I found some of the questions asked during the interview to be seemingly irrelevant and designed to put me on the spot. While I understand the importance of assessing candidates under pressure, it's worth noting that thoughtful responses often require time and consideration. This approach may inadvertently disadvantage candidates who excel in actual day to day problem-solving but may struggle to provide immediate answers to abstract questions.