Asking for feedback is a secretly powerful tool in data work. Let’s talk about why, and how to do it well A recent conversation with a fellow data practitioner sparked an idea that I want to share today. What is your process for conducting data analysis or modeling, and what do you consider important but perhaps unsung parts of getting the job done well? I realized as we were talking that getting feedback from other people as I go through the work is an extremely important part of my process, but it’s not actually something that is explicitly instructed to junior practitioners in my experience.

Learning how to be a professional data scientist is different now, but it’s not impossible Today’s column is partly about data science, but it’s also about the sociology of work. As a senior practitioner in the field, I started my data science career long before Covid-19 and the radical shift in the way we work today developed. I started my professional career years before even that. As a result, my years of learning how to be a professional anything, let alone a professional data scientist, were spent in close quarters with much more experienced people, and in many ways that made it possible for me to get where I am.

Machine Learning Engineers — What Do They Actually Do? Does “Machine Learning Engineer” mean something new to our field? If so, what? The title is a trick question, of course. Much like Data Scientist before it, the title Machine Learning Engineer is developing into a trend in the job market for people in our profession, but there is no consensus about the meaning of the title or the functions and skills it should encompass.