>The data lifecycle is waaay overpopulated with Data Scientists who are not empowered or knowledgeable enough to work with product designers and engineers to do

>The data lifecycle is waaay overpopulated with Data Scientists who are not empowered or knowledgeable enough to work with product designers and engineers to do everything that empowers Data Science and ML. This matches my observations as well. I'm an engineer (The non software kind) at an Industrial plant, I have noticed similar in my involvement with data scientists. I think in a lot of cases it needs to be acknowledged that data scientists are not domain subject matter experts. Very often the data scientists we have worked with lack knowledge I take for granted as an engineer such as knowledge of basic chemistry, physics etc. I can sanity check plant data almost instantly. For example I will know if a material reacts in an endothermic or exothermic manner and can verify that its effect on a temperature prediction model make sense. As a result I often feel like Data Scientists are not empowered to bring their full expertise to bear, they don't understand our process fully and lack a lot "engineering" knowledge to make value added inferences about what their models are demonstrating. Often they can deliver a model and show that a particular term is significant but they have a very shallow understanding of what the term actually represents and can't provide concrete recommendations as to how we could modify our plant to benefit from what their model is demonstrating. Sometimes I feel like we need an additional translator sitting between who can speak both "Data science" and "Engineer" I don't think this is quite what "Data Engineer" as suggested by parent article is but possibly the role could be expanded to incorporate this.