You recognize that you haven't really needed strong mathematical (or coding) skills to create models for some time. Data Scientists add value by knowing how to translate business speak into XGBoost type model and interesting XGBoost model results into business speak. And, frankly, often by being some of the smartest people in the room. The math is occasionally helpful for speaking the language of the XGBoost model. And picking only people who are decent at math (and coding) helps ensure the smart factor. How much of that will really change with AI? I've also seen Business stakeholders try to use the chatbot to bypass the Data Scientist. Typically it's not long before there is a design decision or an interesting result the Business stakeholders don't understand. That's why I think there will be demand for Data Scientists. Not exactly evaluation and monitoring. And definitely not gatekeeping building of LLM solutions. Often the opposite, called in to explain and debug the Business stakeholders' slop.
> You recognize that you haven't really needed strong mathematical (or coding) skills to create models for some time.
And then there goes something like this [1], where researchers failed to control for p-value: "In this particular setting, emergent abilities claims are possibly infected by a failure to control for multiple comparisons. In BIG-Bench alone, there are ≥220 tasks, ∼40 metrics per task, ∼10 model families, for a total of ∼10^6 task-metric-model family triplets, meaning probability that no task-metric-model family triplet exhibits an emergent ability by random chance might be small."
