Applied Scientist applicants have rated the interview process at Amazon with 3.2 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 80% positive. To compare, the company-average is 58.2% positive. This is according to Glassdoor user ratings.
Candidates applying for Applied Scientist roles take an average of 21 days to get hired, when considering 5 user submitted interviews for this role. To compare, the hiring process at Amazon overall takes an average of 31 days.
Common stages of the interview process at Amazon as a Applied Scientist according to 5 Glassdoor interviews include:
Phone interview: 33%
One on one interview: 33%
Skills test: 33%
Here are the most commonly searched roles for interview reports -
had a 1hr phone screen interview
the interviewer asked basic machine learning questions: regression, decision tree, logistic regression, classification problem
1 behaviour question about conflict at work
the interviewer is very helpful
Interview questions [1]
Question 1
how to classify products to categories and sub-categories.
I applied through an employee referral. The process took 2 months. I interviewed at Amazon in Sep 2022
Interview
My interview process include: 1 phone interview (ML+coding+BQ), Virtual onsite which includes 1 presentation, 5 rounds of interviews (ML + coding + BQ); Behavioral questions based on leadership principals are very important, basically every interview session has BQ questions;
Interview questions [1]
Question 1
ML questions cover topics such as data imbalance, collinearity, feature selection, linear regression, logistic regression, L1/L2 norm regularization;
ML application question -- how would you design a recommendation system that recommend books to users;
1st round technical assessment, including questions about machine learning and coding skill test. One hour, one interviewer. 5 minutes introduction and 25 minutes question section, and 30 minutes coding test
Interview questions [1]
Question 1
explain random forest, metrics of logistic regression