Hi all, I come from a math/stats background and naturally enjoy the analytical side of data science — things like modeling, probability, and extracting insights from data (especially unstructured data like text). One area I’m still building up is the engineering side: data pipelines, model deployment (Flask/API), Docker, and cloud (e.g. AWS). With how capable AI tools have become (e.g. helping scaffold pipelines, generate Dockerfiles, debug code, etc.), I’m wondering: Is it reasonable to rely on AI to handle a good portion of the engineering work, so that I can focus more on the math/stats and problem-solving aspects? Or in reality: Do companies still expect data scientists to be quite hands-on with engineering, without using AI? Is there a risk of becoming too dependent on AI and lacking real understanding? Would love to hear from people working in data science / ML roles today. Thanks! submitted by /u/Excellent_Copy4646
Originally posted by u/Excellent_Copy4646 on r/ArtificialInteligence