KBC Data Scientist Interview Questions
& Process
Real candidates share what happened, how many rounds they had,
and how the experience turned out.
Based on 15 interview experiences · FREE TO READ
Candidate interview experiences
First-hand accounts from people who interviewed at KBC.
KBC
Data Scientist
Mid Average Positive experience Accept offer 6 rounds
Interview process
Recruiter call Phone screen Technical screen Technical screen Onsite Recruiter call
Interview formats
Technical Case Behavioral
The process was good and they followed up well. It started with HR, then a chat with a team member, followed by two case-driven interviews. The final stage involved meeting with a general manager, and then HR again as the last step.
Confirmed questions1 question
- Could you answer some general Data Science questions?
KBC
Data Scientist
Entry Average Negative experience No offer 2 rounds
Interview process
Recruiter call Technical screen Take home
Interview formats
Technical Behavioral
I applied online and got an interview scheduled. The first round was with the team lead, not HR. He asked many theoretical machine learning questions and was impatient and arrogant, interrupting me during my intro and answers. The second round was a task where I had to explain my approach and results. He claimed one of my ML answers was wrong, though my professor and ChatGPT confirmed it was correct. He then sent a long email justifying his incorrect answer and rejecting my application. The entire process was unprofessional and unpleasant, the most arrogant interview I've experienced.
Confirmed questions1 question
- Can you explain the AUC-ROC performance metric, its meaning, and how it functions?
KBC
Data Scientist
Entry Difficult Positive experience No offer 2 rounds
Interview process
Recruiter call Technical screen
Interview formats
Behavioral Technical
It began with a personal interview where we discussed goals, past experience, and interests, followed by a technical segment. The technical part involved describing various machine learning and optimisation algorithms, including logistic regression, random forest models, gradient boosting, and linear regression, along with their pros, cons, and specific details.
Confirmed questions6 questions
- Could you explain what logistic regression is and how it functions?
- Tell me about logistic regression, including its advantages, disadvantages, and specifics.
- Can you describe random forest models, covering their pros, cons, and details?
KBC Data Scientist Interview Questions
Quoted word for word from KBC interview reports.
“What are the methods for preventing overfitting in logistic regression?”
Read reports →“What are the differences between machine learning algorithms?”
Read reports →“What are precision and recall, and how do they relate to each other?”
Read reports →“Can you explain the AUC-ROC performance metric, its meaning, and how it functions?”
Read reports →“Could you explain the K-means algorithm and how to determine the value of K?”
Read report →“Can you describe what a confusion matrix is and how it's used?”
Read report →“What strategies can be employed to handle imbalanced datasets?”
Read report →“Can you explain the difference between supervised and unsupervised learning?”
Read report →“Could you explain what logistic regression is and how it functions?”
Read report →Formats, difficulty and experience
Across all 15 KBC interview reports.