Apple Data Mining Interview Questions | Glassdoor

Apple Data Mining Interview Questions

Helpful (10)  

Data Mining Interview

Anonymous Interview Candidate in Austin, TX
No Offer
Positive Experience
Easy Interview


The process took a week. I interviewed at Apple (Austin, TX) in January 2011.


Applied online through apple's website, and received a callback from a recruiter the same week. We set up a phone meeting with the manager for the same week from there.

Phone interview with manager lasted on order of 1/2-1 hour, where we discussed my background and research, as well as what the apple data group does. We then scheduled a 1-day interview with ~6 1 hour interviews with people from the group.

The apple data group appears to be about 8-10 people: team manager, a sub-manager who directs another more hands on QA group, a database guy who they call their statistician, and a half dozen or so recent phd's from a cs group in AI from UT Austin (most of the people all came from this same group and basically know each other).

The 1 hour interviews were all very friendly and non-technical. I was asked maybe 2-3 pretty softball questions. Almost all of them seemed to be questions on how I would handle some problem they've encountered at apple.

Basically the group is in two sub teams (plus the separate QA team). The one group consists of the database guy and appears to be focused on QA'ing test results from apple products made in China. I've done a pretty fair amount of electronics-type QA and I got the impression (taking his comments at face value) that he was kind of winging it and didn't really get his hands dirtier than say running an anova or pca on test results.

The second group works on fraud detection. The bulk of the work is aimed at stopping people who steal credit card information and then use it to buy apple credit, and then sell the apple credits. Once again taking what they said at face value, essentially what they do is maintain a program running naive-bayes (trained on past found credit violations) to weed out potential new threats. It basically outputs something along the lines of an expected risk and the riskier transactions are sent to a human based phone screening. So definitely nothing super mathematical or sophisticated on the algorithm end. I asked one of the newer team members how he spends his day and he basically said looking at data, so I'm sure they are doing some other type of screening also.

All and all the team, especially the fraud side, seemed like a really like-able group. I was a little surprised when I was passed over, especially since the interviews were so easy. But I was given a strong hint when I was informed (I assumed lied to) that the manager was out sick that day and couldn't meet with me. All and all a good experience, definitely less mathematical/technical than I expected both in what the group does and in the interviews themselves.

Interview Questions

  • We do pre-screening on the data to remove fraud threats -- so how do we find a data sample that we can use to determine a real representation of fraud events.   2 Answers
  • Essentially: We use naive bayes (although he didn't really set up the question), and sometimes the probabilities don't work out when there is no data in the area we are looking.   1 Answer
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