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Decathlon Data Scientist Interview Questions and Answers
Decathlon Data Scientist Interview Experiences
1 interview found
Anonymous
(2 Questions)
- Q1. Intermediate level SQL questions using joins and case when
- Q2. String manipulation advanced level question in python
Top trending discussions
Interview questions from similar companies
(2 Questions)
- Q1. What is supervised learning
- Ans.
Supervised learning is a type of machine learning where the model is trained on labeled data to make predictions or decisions.
Uses labeled training data to learn the mapping between input and output variables
The model is trained on a dataset where the correct output is known
Examples include classification and regression tasks
- Q2. What is overfitting
- Ans.
Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization on new, unseen data.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
Example: A decision tree with too many branches that perfectly fits the training d
Skills evaluated in this interview
I applied via Job Portal
A/B Testing, data structures
Interview Preparation Tips
(2 Questions)
- Q1. How to reduce model inference latency
- Ans.
To reduce model inference latency, optimize model architecture, use efficient algorithms, batch processing, and deploy on high-performance hardware.
Optimize model architecture by reducing complexity and removing unnecessary layers
Use efficient algorithms like XGBoost or LightGBM for faster predictions
Implement batch processing to make predictions in bulk rather than one at a time
Deploy the model on high-performance har
- Q2. Different sql joins and their difference
- Ans.
SQL joins are used to combine rows from two or more tables based on a related column between them.
INNER JOIN: Returns rows when there is at least one match in both tables.
LEFT JOIN: Returns all rows from the left table and the matched rows from the right table.
RIGHT JOIN: Returns all rows from the right table and the matched rows from the left table.
FULL JOIN: Returns rows when there is a match in one of the tables.
SEL
Skills evaluated in this interview
I was interviewed in Apr 2021.
(2 Questions)
Round duration - 60 Minutes
Round difficulty - Medium
I was asked two questions in this round . More emphasis was given on the theoretical aspect of the subject in this round .
- Q1. How can you tune the hyperparameters of the XGBoost algorithm?
- Ans.
Hyperparameters of XGBoost can be tuned using techniques like grid search, random search, and Bayesian optimization.
Use grid search to exhaustively search through a specified parameter grid
Utilize random search to randomly sample hyperparameters from a specified distribution
Apply Bayesian optimization to sequentially choose hyperparameters based on the outcomes of previous iterations
- Q2. Can you explain the hyperparameters in the XGBoost algorithm?
- Ans.
Hyperparameters in XGBoost algorithm control the behavior of the model during training.
Hyperparameters include parameters like learning rate, max depth, number of trees, etc.
They are set before the training process and can greatly impact the model's performance.
Example: 'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100
(1 Question)
Round duration - 50 Minutes
Round difficulty - Medium
This round basically tested some fundamental concepts related to Machine Learning and proper ways to implement a model.
- Q1. What is the difference between Ridge and LASSO regression?
- Ans.
Ridge and LASSO regression are both regularization techniques used in linear regression to prevent overfitting by adding penalty terms to the cost function.
Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients (L2 regularization).
LASSO regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients (L1 regularization).
Ridge regression tends to sh...
(1 Question)
Round duration - 50 Minutes
Round difficulty - Medium
This round was based on some basic concepts revolving around Deep Learning .
- Q1. What are outlier values and how do you treat them?
- Ans.
Outlier values are data points that significantly differ from the rest of the data, potentially affecting the analysis.
Outliers can be identified using statistical methods like Z-score or IQR.
Treatment options include removing outliers, transforming the data, or using robust statistical methods.
Example: In a dataset of salaries, a value much higher or lower than the rest may be considered an outlier.
(2 Questions)
Round duration - 30 Minutes
Round difficulty - Easy
This is a cultural fitment testing round .HR was very frank and asked standard questions. Then we discussed about my role.
- Q1. What do you know about the company?
- Q2. Why should we hire you?
Interview Preparation Tips
Tip 1 : Must do Previously asked Interview as well as Online Test Questions.
Tip 2 : Do at-least 2 good projects and you must know every bit of them.
Tip 1 : Have at-least 2 good projects explained in short with all important points covered.
Tip 2 : Every skill must be mentioned.
Tip 3 : Focus on skills, projects and experiences more.
Skills evaluated in this interview
I was interviewed in Apr 2021.
Interview Questionnaire
9 Questions
- Q1. How can you tune the hyper parameters of XGboost,Random Forest,SVM algorithm?
- Ans.
Hyperparameters of XGBoost, Random Forest, and SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.
For XGBoost, important hyperparameters to tune include learning rate, maximum depth, and number of estimators.
For Random Forest, important hyperparameters to tune include number of trees, maximum depth, and minimum samples split.
For SVM, important hyperparameters to tune include ke...
- Q2. What do these hyper parameters in the above mentioned algorithms actually mean?
- Ans.
Hyperparameters are settings that control the behavior of machine learning algorithms.
Hyperparameters are set before training the model.
They control the learning process and affect the model's performance.
Examples include learning rate, regularization strength, and number of hidden layers.
Optimizing hyperparameters is important for achieving better model accuracy.
- Q3. Difference between Ridge and LASSO and their geometric interpretation.
- Ans.
Ridge and LASSO are regularization techniques used in linear regression to prevent overfitting.
Ridge adds a penalty term to the sum of squared errors, which shrinks the coefficients towards zero but doesn't set them exactly to zero.
LASSO adds a penalty term to the absolute value of the coefficients, which can set some of them exactly to zero.
The geometric interpretation of Ridge is that it adds a constraint to the size...
- Q4. How to fit a time series model? State all the steps you would follow.
- Ans.
Steps to fit a time series model
Identify the time series pattern
Choose a suitable model
Split data into training and testing sets
Fit the model to the training data
Evaluate model performance on testing data
Refine the model if necessary
Forecast future values using the model
- Q5. RNN,CNN and difference between these two.
- Ans.
RNN and CNN are neural network architectures used for different types of data.
RNN is used for sequential data like time series, text, speech, etc.
CNN is used for grid-like data like images, videos, etc.
RNN has feedback connections while CNN has convolutional layers.
RNN can handle variable length input while CNN requires fixed size input.
Both can be used for classification, regression, and generation tasks.
- Q6. Two Case studies related to optimisation. One was cost optimization and other one was Revenue optimization. What data would you look at to solve all these. How would you form the objective function.
- Ans.
Answering a question on data and objective function for cost and revenue optimization case studies.
For cost optimization, look at data related to expenses, production costs, and resource allocation.
For revenue optimization, look at data related to sales, customer behavior, and market trends.
Objective function for cost optimization could be minimizing expenses while maintaining quality.
Objective function for revenue opt...
- Q7. Live coding on Time Series Modelling
- Q8. There were some HR questions as well like how would you make someone understand the difference between a classification problem and a prediction problem.
- Q9. Where do you see yourself in 3 years?
Interview Preparation Tips
If you don't know something,better to mention it clearly.
All the very best!
Skills evaluated in this interview
Selected
I applied via Referral and was interviewed before Sep 2022. There were 6 interview rounds.
(1 Question)
- Q1. All the questiones were asked around CV. Mostly problems related to ML, DL, NLP, mathematics behind the algorithms, case studies, alternate solutions of popular use cases etc.
(1 Question)
- Q1. Same as round 1 but this round involved a lot of mathematical functions and derivations of several aspects of ML and DL. Also a lot of case studies were involved
Had to share my screen and they gave live problems to test my knowledge in python
(1 Question)
- Q1. Call with hiring manager mostly on my CV and a lot of case studies.
(1 Question)
- Q1. Typical HR round questions
I applied via LinkedIn and was interviewed in Oct 2023. There were 3 interview rounds.
(1 Question)
- Q1. Why do you want to leave your current job?
SQL coding question. Medium level
Explain my project and then case study regarding launching new apps
Sarthak Chauhan
Selected
I applied via LinkedIn and was interviewed before May 2023. There were 4 interview rounds.
(3 Questions)
- Q1. Linear regression
- Q2. Probability related questions
- Q3. Sampling and AB testing
(2 Questions)
- Q1. Backpropagation in neural network
- Ans.
Backpropagation is a method used to train neural networks by adjusting the weights based on the error in the output.
Backpropagation involves calculating the gradient of the loss function with respect to the weights of the network.
The gradient is then used to update the weights in the opposite direction to minimize the error.
This process is repeated iteratively until the network converges to a solution.
Backpropagation i...
- Q2. Clustering (k-means, DB scan)
1 question on array (sorting related), 1 question on string (hard problem)
(1 Question)
- Q1. Behavioral questions
Ankit Sati
3 Leet code mediums in 30 mins.
(3 Questions)
- Q1. 5 ML questions in 10 mins
- Q2. 5 Stats question in 10 mins
- Q3. 3 LC mediums in 30 minutes
- Ans.
LC mediums refer to LeetCode mediums, which are medium difficulty coding problems on the LeetCode platform.
LC mediums are coding problems with medium difficulty level on LeetCode platform.
Solving 3 LC mediums in 30 minutes requires good problem-solving skills and efficient coding techniques.
Examples of LC mediums include 'Longest Substring Without Repeating Characters' and 'Container With Most Water'.
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