First Principles Thinking for Data Scientists

First principles thinking encourages data scientists to break problems down to their fundamental truths instead of blindly applying standard frameworks and playbooks. This mindset involves questioning core assumptions, such as whether a standard A/B test metric like clicks actually measures the desired business outcome or if a common engagement metric truly reflects product value. Through examples in A/B testing, metrics definition, and model building, it argues that this approach leads to more impactful solutions. This strategic skill is positioned as a key differentiator for data scientists, especially as AI begins to automate more of the tactical execution work.