Lead Data Scientist applicants have rated the interview process at Nielsen with 2.5 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 100% positive. To compare, the company-average is 64.6% positive. This is according to Glassdoor user ratings.
Candidates applying for Lead Data Scientist roles take an average of 10 days to get hired, when considering 2 user submitted interviews for this role. To compare, the hiring process at Nielsen overall takes an average of 24 days.
Common stages of the interview process at Nielsen as a Lead Data Scientist according to 2 Glassdoor interviews include:
One on one interview: 33%
Presentation: 33%
Phone interview: 17%
Group panel interview: 17%
Here are the most commonly searched roles for interview reports -
I applied online. The process took 1+ week. I interviewed at Nielsen (Columbia, MD) in Oct 2016
Interview
The overall experience was good. very relaxed. i got a call to set up the initial screen interview. phone screen, followed by a phone interview, followed by a face to face interview
Interview questions [1]
Question 1
previous experience, knowledge of data analytics, presentations and publications, former employers, education, expectation.
I applied through a recruiter. The process took 1+ week. I interviewed at Nielsen (Bengaluru) in Oct 2024
Interview
The recruiter reached out to me.
First round was a coding round. Post which I had 3 interviews.
I spoke to the Hiring Manager, VP and Director for Data Science Practice.
Interview questions [1]
Question 1
Questions were around my knowledge in DS product building, problem areas and general data interpretation.
I applied through a recruiter. I interviewed at Nielsen (New York, NY) in Apr 2021
Interview
Met with the data science recruiter and the director of the team. Had a technical interview with the director and the staff data scientist on the team. Had a group call with some members of the team. Met with the director's boss.
Interview questions [1]
Question 1
Technical test was a live, timed test where you could use any resources you needed to. It involved a series of short, straightforward Python tasks involving lists/dictionaries followed by a pandas data transformation task in a Google Colab notebook. Then I was asked to analyze a regression task output and any potential issues, followed by some general questions like, what are the pros/cons of random forests vs. gradient boosted forests; if you were trying to predict XYZ, what would you consider using, etc. Most of the other questions were around my past work or other case type questions - how would you solve this or go about doing this?