Decision Science Intern applicants have rated the interview process at Sam's Club with 3.5 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 50% positive. To compare, the company-average is 62.1% positive. This is according to Glassdoor user ratings.
Candidates applying for Decision Science Intern roles take an average of 19 days to get hired, when considering 2 user submitted interviews for this role. To compare, the hiring process at Sam's Club overall takes an average of 11 days.
Common stages of the interview process at Sam's Club as a Decision Science Intern according to 2 Glassdoor interviews include:
One on one interview: 33%
Skills test: 33%
Personality test: 17%
Background check: 17%
Here are the most commonly searched roles for interview reports -
I applied online. The process took 1 week. I interviewed at Sam's Club (Bentonville, AR) in Apr 2022
Interview
The interview process took a little less than one week. 2 interviews held 4-5 days apart. First was a conversation with the Director. Mostly revolved around CV and my projects. Some behavioral questions about handling different situations at work. Second was an interview with my manager. These were mostly technical questions, and some questions aimed at testing my thought process
Interview questions [1]
Question 1
1. How would you handle the delivery of two urgent tasks?
I applied online. The process took 4 weeks. I interviewed at Sam's Club in Nov 2023
Interview
1 round via karat (outsourced technical interview; lowkey hard; lots of ML fundamentals/probability theory questions + a coding question. I thought I bombed this section of the interview but moved on to the next round so I guess it went better than I thought), 2 rounds w different members of the team (data science managers; each one asked some case study-style ML questions, i.e. what model would you use in this situation/how to interpret it), last round was ~15 mins w the hiring manager and v laid back (all behavioral)
Interview questions [1]
Question 1
lots of stuff about expected value, regularization, how to interpret a logistic regression vs. linear regression, etc.