So that's the real role of the expert here
I work in the data science industry - as a programmer, not a data scientist or statistician. From my general understanding of the field what you're describing is a broadly accepted assumption. But I might be misled by the fact that the company I work for bases its product on this assumption, so I'm not sure if you're just describing this thing from another angle or if there's a different point that I'm missing here or if, in fact, many people spend too much effort trying to hand-tune models.
The data scientists I work with make predictive models in two stages. The first one is to invent (or choose) "features", which include natural variables from the original dataset, functions acting on one or more variables, or supplementary datasets that they think are relevant. The data scientist applies their understanding of statistics as well as domain knowledge to tell the computer which things to look for and which are clearly false positives to be ignored. And the second stage is to build the actual models using mostly standard algorithms like Random Forest or XGBoost or whatnot, where the data scientist might tweak arguments but the underlying algorithm is generally given and doesn't allow for as much user choice.
A common toy example is the Titanic dataset. This is a list of passengers on the Titanic, with variables like age, name, ticket class, etc.. The task is to build a model that predicts which ones survived when the ship sank. A data scientist would mostly work on feature engineering, e.g. introducing a variable that deduces passenger's sex from their name, and focus less on model tuning, e.g. determining the exact weight that should be given to the feature in the model (women and children had much higher rates of survival).
In a more serious example, a data scientist might work on figuring out which generic datasets are relevant at all. Suppose you're trying to predict where to best open a new Starbucks branch. Should you look at the locations of competing coffee shops? Noise from nearby construction? Public transit stops or parking lots? Nearby tourist attractions or campuses or who knows what else? You can't really afford to look at everything, it would both take too long (and maybe cost too much) and risk false positives. A good domain expert is the one who generates the best hypotheses. But to actually test those hypotheses, you use standard algorithms to build predictive models, and if a simple linear model works, that's a good thing - it shows your chosen features were really powerful predictors.
It should come as no surprise to people on this list that models often outperform experts. But these are generally finely calibrated models, integrating huge amounts of data, so this seems less surprising. How can the poor experts compete against that?
But sometimes the models are much simpler than that, and still perform better. For instance, the models could be linear, rather than having higher order complexities. These models can still outperform experts, because in practice, despite their beliefs that they are doing a non-linear task, expert decisions can often best be modelled as being entirely linear.
But surely the weights of the linear models are subtle and need to be set exactly? Not really. It seems that if you take a linear model, and weigh the variables by +1 or -1 depending on whether it has a positive or negative impact on the result, then you will get a model that still often outperforms experts. These models with ±1 weights are called improper linear models.
What's going on here? Well, there's been a bit of a dodge. I've been talking about "taking" a linear model, with "variables", and weighing the factors depending on a positive or negative "impact". And to do all that, you need experts. They are the ones that know which variables are important, and know the direction (positive or negative) in which they impact the result. They don't choose these variables by just taking random possibilities and then figuring out what the direction is. Instead, they understand the situation, to some extent, and choose important variables.
So that's the real role of the expert here: knowing what should go into the model, what really makes the underlying dependent variable change. Selecting and coding the variable information, in the terms that are often used.
But, just as experts can be very good at that task, they are human, and humans are terrible at integrating lots of information together. So, having selected the variables, they get regularly outperformed by proper linear models. And when you add the fact that the experts have selected variables of comparable importance, and that these variables are often correlated with each other, it's not surprising that they get outperformed by improper linear models as well.