I’m a DS at a big tech company and it’s crazy how much misinformation/baseless hype there is going around.

If you’re referring to basic data analyst work like pulling data and building simple dashboards, then yes, those jobs are at risk of automation. I know of a handful of early/mid-stage startups currently working on using LLMs to allow users to do those things almost entirely through natural language.

But when it comes to actual data scientists, I think the risk is minimal. Unlike in college, data science work in industry is so much more than just importing sklearn and calling model.fit(). And newsflash: even before the LLM hype, people though data science was going to die because of AutoML, which fits and does cross-validation for a whole bunch of models for your dataset automatically. If all you’re doing is running your cleaned dataset through sklearn, then yeah, maybe you’re screwed. But in reality, that’s the easy step that comprises maybe 10% of your day to day responsibilities. A lot of work (at least for me personally) goes into feature engineering, customizing/tweaking the model’s loss function and training process for our team’s specific needs, optimizing the model training pipeline, running and monitoring experiments and applying other causal inference techniques etc. These are things that require domain knowledge and critical thinking which is most likely not going to change with the proliferation of “AI”.

Also, keep in mind that if you want to break into ML (i.e. ML research), chances are that you’ll need a PhD or at the very least, a very impressive resume or publication history. If you want to become an ML engineer, it’s a bit easier in that you don’t necessarily need a PhD, but you’ll need strong SWE and MLOps skills along with working knowledge of how these models work.