As part of my hiring process, I have prospective data science candidates perform a data challenge. The data challenge asks the candidate to take the data provided, go through the model development process, and end with a classification model. Unless I’m hiring at a senior level, I’m not looking for the entire data science workflow to be perfect — at a high-level, I look for a thoughtful, logical approach to the problem at hand. This is usually exposed in the first 50 lines of code, where candidates are dropping features based on an arbitrary threshold of missing values. I’ve seen…

Brett Nebeker

ML & Data Products @ Lighthouse | Data & Analytics Consultant | Data Science Mentor

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