Restricted category To view older posts, you must first submit an approved post in that category

Google DS - does anyone know about this?

Looking for information on the google DS process - does anyone know how it works? can you share resources or questions if you have them?

been a while since I interviewed and ended up going with a different company. I rmember them asking me a probability question…it was something like “there are (x amount) of cards with (x different colors) and (x different numbers). each color has one of each number. if you pick 2 different cards, what’s the probability that they aren’t the same color or number?”

sorry if that sounds confusing, I dont remember the number of cards/colors and stuff. Other than that, just brush up on your basic data science knowledge, learn about Google’s culture, etc…good luck!

Here’s a case question I got:

Google wants to increase the font size of the sign up button on Gmail

  1. How do you choose KPIs to track?
  2. How do you select the size of the experiment?
  3. What’s the time interval you want to test?
  4. How do you plan on designing the reporting dashboard?
  5. How can you confirm the effects are real and long term?

Other interview parts (high level and non-specific because of NDA)

Statistics: asked about terms, should be highly proficient in basic concepts (p-value, confidence interval, logistic regression, linear regression).
A/B Testing: understand be able to explain pitfalls, biases, and edge cases
SQL: extensively practice calculation topics
ML: know what model to use when, be familiar with basic assumptions, consider pros and cons of each model, consider errors that might exist
Behavioral: concisely talk about past experiences, checked out Candor’s Google questions and got asked a few of the same ones (https://candor.co/interviews/data-scientist/google)

1 Like

Got my offer a few days ago, but here are some questions I got during the various rounds:

  • Tell me about projects you have worked on in the past
  • Follow ups on technical aspects of the projects and difficulties faced/what I learned
  • A/B test questions asking what test to use, how to calculate sample sizes, power of test (pretty standard questions overall)
  • SQL and Python questions
  • Time series questions such as application of Bayesian time series
  • Questions on linear regression focusing on assumptions for different scenarios

@datalover21 @angie_swe this is super super helpful!! thank you so much for sharing

Check out my post for my interview timeline and experience! I DID IT! Google Product Analyst / Data Science end to end