I engaged in an autoethnographic exercise, working as an mTurker to “systematically analyze” the world of crowdworkers. Using myself as both instrument and unit of analysis, I spent a day working on the mTurk platform (I also tried out Crowdflower), navigating the idiosyncrasies of the platform and trying to optimize my earnings against the time I spent performing tasks. I asked: How does crowdwork affect data science products? And what do data scientists need to know about crowdworking that would help them design tasks that support better machine learning?