Data Scientist applicants have rated the interview process at Integral Ad Science with 3 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 44.3% positive. This is according to Glassdoor user ratings.
Candidates applying for Data 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 Integral Ad Science overall takes an average of 22 days.
Common stages of the interview process at Integral Ad Science as a Data Scientist according to 5 Glassdoor interviews include:
One on one interview: 27%
Group panel interview: 20%
Phone interview: 13%
Skills test: 13%
Background check: 13%
IQ intelligence test: 7%
Presentation: 7%
Here are the most commonly searched roles for interview reports -
I applied through a recruiter. The process took 3 weeks. I interviewed at Integral Ad Science (New York, NY) in Mar 2016
Interview
Three stage interview process. 1) On-site 30-45 min technical interview. 2) Data analysis assignment/report with a week to do. 3) On-site long interview that starts with discussing the data analysis assignment results/report with all the team members and then with many one-on-one meetings with team members.
Interview questions [1]
Question 1
Questions on probability, algorithms, map reduce, machine learning, ad tech related problems.
First there was a Recruiter Human Resources phone screen, then a Hiring Manager interview, a panel with two to three other current data scientists, and a technical round. Pretty standard and straight forward.
I applied through a recruiter. I interviewed at Integral Ad Science (New York, NY)
Interview
I had a recruiter phone screen, another phone screen with 2 DS's (included some easy live-coding on a shared screen), a homework assignment, and an on-site.
The on-site portion was a pretty typical interview for a Senior (or non-Junior) DS role. I was given a 'homework assignment' beforehand, and I walked the entire DS team through my solution - they asked about my method and quizzed me on what I thought the next steps would be.
The 1:1 interviews were the usual mix - algorithmic whiteboarding in Python, talking about Machine Learning projects, whiteboard probability problems, and talking to some of the DS's about their projects and how I would think about them.
Everybody I spoke to was extremely smart and very nice.
Interview questions [2]
Question 1
We're trying to do ____. What do you see as the challenges here? How would you approach this problem if you were in charge of solving it?