IBM Data Analyst/Data Scientist interview questions
based on 5 ratings - Updated Apr 2, 2026
Averageinterview difficulty
Mostly positiveinterview experience
How others got an interview
67%
Applied online
Applied online
33%
Recruiter
Recruiter
Interview search
5 interviews
IBM interviews FAQs
Data Analyst/Data Scientist applicants have rated the interview process at IBM with 2.7 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 33% positive. To compare, the company-average is 66.3% positive. This is according to Glassdoor user ratings.
Candidates applying for Data Analyst/Data Scientist roles take an average of 3 days to get hired, when considering 3 user submitted interviews for this role. To compare, the hiring process at IBM overall takes an average of 30 days.
Common stages of the interview process at IBM as a Data Analyst/Data Scientist according to 3 Glassdoor interviews include:
Phone interview: 50%
Skills test: 50%
Here are the most commonly searched roles for interview reports -
I applied through a recruiter. I interviewed at IBM (Bengaluru) in Nov 2024
Interview
First round was coding sql and python. Second round i was given raw data and asked to provide insights in not more than 5 slides. Third round was managerial - where 3 people from the US took it.
I applied online. I interviewed at IBM (Bengaluru) in Jul 2023
Interview
Cleared all rounds and background verification and documentation also done but still didn’t receive any offer letter. Hr told me that it’s in process meanwhile it’s immediate joiner position. Its been more than a month now. No response from HR. Time wasting experience till now in my 6 years of corporate career.
I applied online. The process took 3 days. I interviewed at IBM (New York, NY) in Mar 2022
Interview
I was sent a programming assignment in the form of a Jupyter Notebook. The whole process took about 3 hours and was heavily focused on data visualization and data exploration.
Interview questions [1]
Question 1
Graph feature importances of machine learning models to present to stakeholders.