During my time as a Data Scientist, I had the chance to interview my fair share of candidates for data-related roles. While doing this, I started noticing a pattern: some kinds of (simple) mistakes were overwhelmingly frequent among candidates! In striking disagreement with a famous quote by Tolstoy, it seems to me, “most unhappy mistakes in case studies look alike”.

In my mind, I started picturing the kind of candidate that I would hire in a heartbeat. No, not a Rockstar/Guru/Evangelist with 12 years of professional experience managing Kubernetes clusters and working with Hadoop/Spark, while simultaneously contributing to TensorFlow’s development, obtaining 2 PhDs, and publishing at least 3 Deep Learning papers per year. Nope; I would just instantly be struck by a person who at least does not make the kind of mistakes I am about to describe… And I can imagine the same happening in other companies, with other interviewers.

Although this is a personal and quite opinionated list, I hope these few tips and tricks can be of some help to people at the start of their data science career! I am putting here only the more DS-related things that came to my mind, but of course writing Pythonic, readable, and expressive code is also something that will please immensely whomever is interviewing you!

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