For those working in consulting as Data Scientists or AI/ML engineers — how much of your role actually involves advanced ML vs. simpler stats and a lot of data engineering? In my experience, most client needs are met without building complex models. Curious to hear how it is for others

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My running theory is that 99% of observational analytics can be solved with a Histogram (sometimes on a timeline).

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Yeah snapshot data!

Re: predictive models i find some element of modelling is significantly more effective than basic statistics.

Why? Because stats tend to be observational and only give you the pure probability of occurrence P(A) of some event. That's good for population studies but not good for individual predictions where you want to identify the likelihood of a choice, while the observed pay has some other set of priors... i.e. where you want P(A|B), where B can be heavily nonlinear in its relationship with A.

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The real question here is... what do you consider "complex" models? Do you consider XGBoost complex?

Looool AE1 half of them don't know the difference between mean and median.

This is where your knowledge comes in, which helps your clients to make it less complicated and easier to understand for them

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