A Career in Data Is Not Always a Straight Line, and That’s Okay

A career in data is no longer a straight line, requiring professionals to evolve beyond generalist roles into specialists with domain knowledge. To remain relevant, data scientists must master deployment, LLMs, and RAG, as basic tasks are increasingly automated by AI agents. Flexibility has become a crucial skill, allowing practitioners to adapt to new trends rather than focusing on a single tool. While LLMs are useful for productivity, there is a risk of outsourcing human thinking and losing intuition, especially in critical fields like medical imaging. The field should shift its focus from chasing new tools toward better science and more meaningful applications.