How are your interviews like real-world data science problems? 🤔

In predictive models, the most important feature is often whether the user has already done the action you’re trying to predict. Now put yourself in the shoes of the hiring manager trying to predict who will be able to solve their business problems: You can reduce the “risk” of your candidacy by demonstrating that you’ve already tackled similar challenges. This is why we often recommend students and those transitioning to data science to work on projects as much as possible.

But what if your classroom projects aren't related to the industry? 

A hugely valuable skill is recognizing the transferability of your experience. For example, I analogized my work at the SEC—collaborating with enforcement lawyers on high-stakes cases—to working with senior executives on critical data projects. Similarly, I easily explained how my academic research in education policy mirrored the causal inference problems tech companies face using examples that anyone could understand. (A huge benefit to education research is how well the challenges and problems are universally understood.) 

I dive into this in more detail in this video: https://www.youtube.com/shorts/TM5G1KWtcnM