Last year, I wrote an article How AI Is Rewriting the Day-to-Day of Data Scientists. In that article, I summarized the AI impact on data scientists as eliminating low-value tasks and accelerating high-value work. A year has passed. AI has advanced from GPT 4.5/Claude 4 to GPT 6/Claude 5.5, and I’ve changed jobs (twice). It is time to revisit what has changed — not just how data scientists use AI today, but how AI has transformed the job itself.

AI eliminated much of the mechanical work — but not the need for data scientists. Over the past year, I have seen this happen on multiple fronts. I have barely written any SQL or Python manually in the past six months. The only time I write SQL from scratch is for a quick SELECT * LIMIT 100 table inspection. AI writes most analysis code. My role has changed from the coder to the reviewer. Repeated workflows are increasingly packaged into Agent Skills. Stakeholders pull more data and run more analysis themselves. Semantic layers become more important for AI reliability. The bottleneck moved from “Can someone get the data?” to “Can we trust what they got?”

AI expanded the definition of data science. Last year, I used AI more as a coding assistant. However, things have changed at an extremely fast pace, with far more depth and breadth than I expected. AI dramatically reduced the analysis turnaround time.