Hey everyone, I’m a Data Scientist with a heavy background in Mathematics and Statistics. To be honest, I’ve always loved the theoretical side—deriving logic, experimental design, and rigorous validation—but I’ve always struggled with (and frankly, disliked) the “engineery” side of the job. Things like building complex data pipelines, Dockerizing models, writing FastAPI wrappers, and setting up CI/CD have always been my biggest bottlenecks. Recently, I’ve started using LLMs (Claude/GPT-4) almost like a “Junior DevOps Engineer.” I find that if I handle the mathematical architecture and logic, the AI is incredibly good at generating the boilerplate for the infrastructure and deployment side. It’s finally allowing me to focus 90% of my time on the stats/math work I actually enjoy, while still delivering “production-ready” code. Is anyone else with a similar background doing this? Or am I setting myself up for a fall by “outsourcing” the engineering tasks to AI? Curious if you think this “Manager of AI” workflow is the future for specialists, or if I still need to bite the bullet and learn the deep plumbing of Software Engineering. My questions for the community: Is this “Architect + AI Assistant” workflow seen as a viable long-term strategy for specialists, or is it a “crutch” that will eventually backfire in senior roles? For those in hiring/lead roles: Would you rather have a DS who is a math genius but relies on AI for deployment, or a “full-stack” DS who is mediocre at both? What are the “silent killers” I should watch out for when letting AI handle my data pipelining and deployment logic? submitted by /u/Excellent_Copy4646
Originally posted by u/Excellent_Copy4646 on r/ArtificialInteligence