What’s commonly expected from a data scientist is a combination of subject matter expertise, mathematics, and computer science. This is a tall order and it makes sense that there would be a shortage of people who fit the description. The more knowledge you have, the better, however, I’ve found that the skillset you need to be effective, in practice, tends to be more specific and much more attainable. This approach changes both what you look for from data science and what you look for in a data scientist.
A background in computer science helps with understanding software engineering, but writing working data products requires specific techniques for writing solid data science code. Subject matter expertise is needed to pose interesting questions and interpret results, but this is often done in collaboration between the data scientist and subject matter experts (SMEs). In practice, it is much more important for data scientists to be skilled at engaging SMEs in agile experimentation. A background in mathematics and statistics is necessary to understand the details of most machine learning algorithms, but to be effective at applying those algorithms requires a more specific understanding of how to evaluate hypotheses…
We tend to judge data scientists by how much they’ve stored in their heads. We look for detailed knowledge of machine learning algorithms, a history of experiences in a particular domain, and an all-around understanding of computers. I believe it’s better, however, to judge the skill of a data scientist based on their track record of shepherding ideas through funnels of evidence and arriving at insights that are useful in the real world.
