Was the data scientist role only about building NLP models?
Are the LLms gonna build Churn prediction models?
Tell the PM why stopping the A/B test halfway through is a bad idea?
Push back on loony ideas of applying ML to predicting sales from user horoscopes?
Maybe the role is a bit tinier in scope than 10 years ago, but I see it as a good thing. If you looked at DS positions on job search sites the role descriptions would be all over the place, maybe now at least we'll see it consolidate.
Exactly - in my company we had some NLP models in Customer Service (bag-of-words for classifying tickets) but everywhere else it was just classification or regression problems.
So yeah, the bag-of-words model got replaced with a chatbot several years ago (when chatbots were all the rage back in like 2017) and will probably get replaced again with an LLM-enhanced chatbot soon. But the meat and potatoes are those classification and regression models and they aren't going anywhere.
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I don't understand the framing of the assumption.
Was the data scientist role only about building NLP models? Are the LLms gonna build Churn prediction models? Tell the PM why stopping the A/B test halfway through is a bad idea? Push back on loony ideas of applying ML to predicting sales from user horoscopes?
Maybe the role is a bit tinier in scope than 10 years ago, but I see it as a good thing. If you looked at DS positions on job search sites the role descriptions would be all over the place, maybe now at least we'll see it consolidate.
Exactly - in my company we had some NLP models in Customer Service (bag-of-words for classifying tickets) but everywhere else it was just classification or regression problems.
So yeah, the bag-of-words model got replaced with a chatbot several years ago (when chatbots were all the rage back in like 2017) and will probably get replaced again with an LLM-enhanced chatbot soon. But the meat and potatoes are those classification and regression models and they aren't going anywhere.