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CIMB bank Data Scientist Interview Questions and Answers
CIMB bank Data Scientist Interview Experiences
2 interviews found
Anonymous
Model Training based on the dataset and problem statement provided.
(2 Questions)
- Q1. Asked about Roc Auc
- Q2. What is difference between supervised and unsupervised learning
- Ans.
Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.
Supervised learning requires a target variable to predict, while unsupervised learning does not.
In supervised learning, the model learns from labeled examples, while in unsupervised learning, the model finds patterns in data.
Examples of supervised learning include regression and classification tasks, while clusteri
Skills evaluated in this interview
I applied via Approached by Company and was interviewed in May 2024. There was 1 interview round.
(2 Questions)
- Q1. Xgboost in-depth
- Ans.
Xgboost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
Xgboost stands for eXtreme Gradient Boosting, which is an optimized implementation of gradient boosting.
It is widely used in Kaggle competitions and other machine learning tasks due to its high accuracy and efficiency.
Xgboost uses a technique called boosting, where multiple weak learners are combined to create...
- Q2. Type 1 & 2 error.
Skills evaluated in this interview
Top trending discussions
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Selected
I was interviewed in Mar 2023.
(2 Questions)
- Q1. Python basic concepts
- Q2. ML algorithm rule, scoring models, library details, statistics questions
Interview Preparation Tips
Absolute waste of time and energy with this Company
Save yourself and find better places to work
I applied via LinkedIn and was interviewed in Oct 2021. There were 5 interview rounds.
Interview Questionnaire
1 Question
- Q1. Questions based on various machine learning evaluation metrics.
Interview Preparation Tips
Read about various banking case studies.
Elaborate over your interest in banking domain if you have no prior experience in the same.
I applied via LinkedIn and was interviewed in Oct 2021. There were 5 interview rounds.
Interview Questionnaire
1 Question
- Q1. Various machine learning evaluation metrics
- Ans.
Machine learning evaluation metrics are used to measure the performance of a model.
Accuracy
Precision
Recall
F1 Score
ROC Curve
AUC
Confusion Matrix
Mean Squared Error
Root Mean Squared Error
R-squared
Interview Preparation Tips
Read more about the banking domain case studies.
Elaborate over your interest in banking domain if you don't have prior experience in this domain.
Skills evaluated in this interview
Selected
I applied via Campus Placement and was interviewed in Dec 2022. There were 4 interview rounds.
Hard level aptitude questions... Time taking sums
(2 Questions)
- Q1. Puzzles and probability questions
- Q2. Asking completely about data science projects in your resume
(2 Questions)
- Q1. Just asked some puzzle questions
- Q2. Asked if iam willing to relocate and about my family
Interview Preparation Tips
- Data Science
(2 Questions)
- Q1. Logistic regression loss function
- Q2. Step function in ML context
- Ans.
Step function is a function that returns a constant value for a certain range of inputs.
In machine learning, step functions are used as activation functions in neural networks.
They are typically used in binary classification problems where the output is either 0 or 1.
Examples include Heaviside step function and sigmoid step function.
Skills evaluated in this interview
(1 Question)
- Q1. What do you do if your model has higher false positives
- Ans.
Investigate the model performance metrics and adjust the threshold for classification.
Analyze the confusion matrix to understand the distribution of false positives.
Adjust the threshold for classification to reduce false positives.
Consider using different evaluation metrics like precision, recall, and F1 score.
Explore feature importance to identify variables contributing to false positives.
Skills evaluated in this interview
I applied via Job Portal and was interviewed in Aug 2023. There were 2 interview rounds.
Aptitude test for about an hour.
(2 Questions)
- Q1. Questions related to data science were asked based on your resume. Puzzles were asked too. Mostly they were from gfg.
- Q2. What are parameters used in a random forest.
- Ans.
Parameters used in a random forest include number of trees, maximum depth of trees, minimum samples split, and maximum features.
Number of trees: The number of decision trees to be used in the random forest.
Maximum depth of trees: The maximum depth allowed for each decision tree.
Minimum samples split: The minimum number of samples required to split a node.
Maximum features: The maximum number of features to consider when
Interview Preparation Tips
Skills evaluated in this interview
(2 Questions)
- Q1. Explain previous projects
- Q2. Explain 2 algo of your choice
Interview Preparation Tips
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CIMB bank Data Scientist Interview Process
based on 2 interviews
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Data Scientist Interview Questions from Similar Companies
CIMB bank Data Scientist Reviews and Ratings
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