2 min readfrom Data Science

I bombed Google DS Research, so you dont have to

Our take

In this candid reflection, the author shares their experience with Google’s Data Science Research interview, highlighting the two critical rounds: Statistical Knowledge and Data Analytics. The statistical portion challenged their understanding of distributions, expectations, and confidence intervals, ultimately revealing that what seemed complex had a straightforward answer. The second round involved diagnosing a flawed model, where expectations of a case study shifted unexpectedly to a technical equation task. The key takeaway?

Two rounds: 1. Statistical Knowledge 2. Data Analytics and Intuition

For statistical knowledge, it was a complex question, but actually had a simple answer.

It required you to have through knowledge of distribution, expectations and confidence intervals.

The key challenge was to identify what was the distribution of the data, from a sample, generalize it to the population and find the confidence interval.

Looking back, it was a easy question, but I definitely took wayyyy to much time to get to the answer. They for sure test for Googlyness. I would assume the interviewer had multiple questions in mind but I never got to the next one. Soo no hire.

For the data analysis and Intuition, I was expecting a case study, on experimentation or ML. It was kind off an hybrid. It involved diagnosing a flawed model, how to improve it, and what other methods would work better. This part was fine, not too bad.

What caught me off guard was, they asked me to write the equation MLE for 2 models, one general and one a niche. Honestly I dint know, lol.

Well, learnings ? Practice your Stats and ML like you are writing a school exam.

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