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
- How do you choose KPIs to track?
- How do you select the size of the experiment?
- What’s the time interval you want to test?
- How do you plan on designing the reporting dashboard?
- 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)
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
Check out my post for my interview timeline and experience! I DID IT! Google Product Analyst / Data Science end to end