The two biggest killers in the world of ML projects have nothing to do with what most data scientists ever imagine. These killers aren’t related to algorithms, data, or technical acumen. They have absolutely nothing to do with which platform you’re using, nor with the processing engine that will be optimizing a model. The biggest reasons for projects failing to meet the needs of a business are in the steps leading up to any of those technical aspects: the planning and scoping phases of a project.
Throughout most of the education and training that we receive leading up to working as a DS at a company, emphasis is placed rather heavily on independently solving complex problems. Isolating oneself and focusing on showing demonstrable skill in the understanding of the theory and application of algorithms trains us to have the expectation that the work we will do in industry is a solo affair. Given a problem, we figure out how to solve it.