A thinly veiled sales pitch for a "data science collaboration platform" is trotted out as some profound measure of how attractive a given company is to prospect

A thinly veiled sales pitch for a "data science collaboration platform" is trotted out as some profound measure of how attractive a given company is to prospective applicants for data science positions. I'm wary of the Joel Test and others like it for one very simple reason: the points are goals to strive towards, not some filter by which prospective employers should be judged. Especially in the case of a fledgeling field such as data science, wouldn't you want to be deeply involved in laying down the foundation for all that this screener list seems to take for granted? Almost every point on this list is far from a solved problem. This is an exciting time for the industry, a happening time when we begin to establish some semblance of best practices by groping in the dark. What good are prospective data scientists who recoil from the challenge of working with engineers on tools that will enable future research?