Data Science Interviews are About the Audience, Not the Math

I have been on both sides of the data science interview process; therefore, I know how stressful it is for the applicant, but also how important it is for the company to find the right candidate. Over the years, I have observed that anticipating audience needs is the most important factor at each interview stage; yet data science candidates often over-index on technical acumen, and neglect the fact that every evaluator is reviewing different attributes. Worse than that, data science candidates tend to go down rabbit holes such as Bayesian parameter estimation. Going this deep into a highly technical niche subject has two potential risks: Having an interviewer's eyes glaze over because they are not the right audience, or worse, the interviewer has a deeper technical understanding about that particular subject and will trip you up in your answer! In my 11 years building a data science career, I have found there to be four main stages to an interview process, each with distinct audiences: the initial phone screen, the technical evaluation, the "take home" assignment, and a behavioral assessment.