Carrefour Data Scientist Interview Questions
& Process
Real candidates share what happened, how many rounds they had,
and how the experience turned out.
Based on 17 interview experiences · FREE TO READ
Candidate interview experiences
First-hand accounts from people who interviewed at Carrefour.
Carrefour
Data Scientist Intern
Intern Average Positive experience Accept offer 2 rounds
Interview process
Recruiter call Technical screen Offer
Interview formats
Technical Coding Behavioral
Had an HR call first, then an interview with two engineers where we did some online coding in python and talked about basic machine learning concepts. After waiting for about a month, I got the final offer.
Confirmed questions2 questions
- Can you explain why the gradient vanishing problem occurs and what are the common ways to solve it?
- Describe RMSNorm and its mechanism.
Carrefour
Data Scientist
Entry Average Negative experience Decline offer 2 rounds
Interview process
Recruiter call Technical screen Onsite
Interview formats
Technical Behavioral
First interview was a video call, with technical questions right away. The interviewer was unpleasant and seemed unsure about their own questions. I was offered a second interview at the office.
Confirmed questions1 question
- Regarding the primitive of ln(x), describe the method used to get the answer.
Carrefour
Data Scientist
Entry Average Positive experience Accept offer 5 rounds
Interview process
Recruiter call Technical screen Phone screen Group Technical screen
Interview formats
Technical Behavioral Coding Group
The interviews lasted 2 months and went like this: Online questionnaire in 3 steps: Candidate profiling. Introduction videos: Why choose the graduate program, why Carrefour, what is a good leader, a question in English. Technical test: Hackerrank, SQL, Python, statistics, ML (MCQ). Video interview with HR and a data analyst: why Carrefour and why the graduate program, past experiences, etc. Assessment center: Group project: evaluation related to a business case. Technical interview: 2.5 hours of Python, algorithms, and ML.
Confirmed questions1 question
- Can you explain the difference between bagging and boosting?
Carrefour Data Scientist Interview Questions
Quoted word for word from Carrefour interview reports.
“What is the loss function for logistic regression?”
Read reports →“Describe RMSNorm and its mechanism.”
Read reports →“Can you explain the difference between bagging and boosting?”
Read reports →“Can you explain why the gradient vanishing problem occurs and what are the common ways to solve it?”
Read reports →“Can you detail the principles behind machine learning algorithms?”
Read report →“What makes a good leader?”
Read report →“Why the graduate program?”
Read report →“How would you handle an imbalanced dataset?”
Read report →“Could you elaborate on the detailed workings of diverse machine learning algorithms, perhaps by explaining the node selection process in a decision tree?”
Read report →Formats, difficulty and experience
Across all 17 Carrefour interview reports.