AmbitionBox
Interview Questions
- Reviews
- Salaries
- Interview Questions
- About Company
- Benefits
- Jobs
- Office Photos
- Community
Add office photos
Filter interviews by
Red Tag Data Scientist Interview Questions and Answers
Red Tag Data Scientist Interview Experiences
1 interview found
Anonymous
Selected
I applied via Indeed and was interviewed before Oct 2022. There were 3 interview rounds.
(1 Question)
- Q1. Tell about yourself How many rows and columns in excel sheet Vlookup
- Ans.
I am a data scientist with expertise in Excel and Vlookup.
An Excel sheet can have up to 1,048,576 rows and 16,384 columns.
Vlookup is a function in Excel used to search for a value in the first column of a range and return a value in the same row from another column.
For example, =VLOOKUP(1001, A1:B10, 2, FALSE) will search for the value 1001 in column A and return the corresponding value in column B.
(1 Question)
- Q1. Tell me about your self
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
(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
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'.
Interview Preparation Tips
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
Navina Kaur
Selected
I applied via Referral and was interviewed before Jun 2023. There were 3 interview rounds.
(1 Question)
- Q1. Resume related questions with the hiring manager
Python for Data Science basics
Optimization case study
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 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.
The overall parameters have been divided into 3 categories by XGBoost authors:
General Parameters: Guide the overall functioning
Booster Parameters: Guide the individual booster (tree/regression) at each step
Learning Task Parameters: Guide the optimization performed
The various steps to be performed for Parameter Tuning are:
1) Choose a relatively high learning rate. Generally a learning rate of 0.1 works but somewhere bet... - Q2. Can you explain the hyperparameters in the XGBoost algorithm?
- Ans.
1) Hyperparameters are certain values or weights that determine the learning process of an algorithm.
2) XGBoost provides large range of hyperparameters. We can leverage the maximum power of XGBoost by tuning its hyperparameters.
3) The most powerful ML algorithm like XGBoost is famous for picking up patterns and regularities in the data by automatically tuning thousands of learnable parameters.
4) In tree-based models, l...
(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 uses two different penalty functions. Ridge uses L2 where as lasso go with L1. In ridge regression, the penalty is the sum of the squares of the coefficients and for the Lasso, it’s the sum of the absolute values of the coefficients. It’s a shrinkage towards zero using an absolute value (L1 penalty) rather than a sum of squares(L2 penalty).
As we know that ridge regression can’t have zero coef...
(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, or simply outliers, are data points in statistics that don’t belong to a certain population. An outlier value is an abnormal observation that is very much different from other values belonging to the set.
Identification of outlier values can be done by using univariate or some other graphical analysis method. Few outlier values can be assessed individually but assessing a large set of outlier values requ...
(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?
- Ans.
General Tip : Before an interview for any company , have a breif insight about the company , what it does , when was it founded and so on . All these info can be easily acquired from the Company Website itself .
- Q2. Why should we hire you?
- Ans.
Tip 1 : The cross questioning can go intense some time, think before you speak.
Tip 2 : Be open minded and answer whatever you are thinking, in these rounds I feel it is important to have opinion.
Tip 3 : Context of questions can be switched, pay attention to the details. It is okay to ask questions in these round, like what are the projects currently the company is investing, which team you are mentoring. How all is the...
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
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
Red Tag Interview FAQs
Tell us how to improve this page.
Interview Questions for Popular Designations
- Senior Data Scientist Interview Questions
- Jr. Data Scientist Interview Questions
- Decision Scientist Interview Questions
- Associate Data Scientist Interview Questions
- Data Scientist Intern Interview Questions
- Lead Data Scientist Interview Questions
- Trainee Decision Scientist Interview Questions
- Research Scientist Interview Questions
- Show more
Red Tag Data Scientist Interview Process
based on 1 interview
Interview experience
Interview Questions from Similar Companies
|
Retail Store Manager
15
salaries
|  ₹7 L/yr - ₹15.6 L/yr |
|
Sales Executive
7
salaries
| ₹3 L/yr - ₹4.4 L/yr |
|
Merchandise Planner
5
salaries
| ₹11 L/yr - ₹15 L/yr |
|
Shop Manager
4
salaries
| ₹10.5 L/yr - ₹22 L/yr |
|
Assistant Manager Merchandising
4
salaries
| ₹10.8 L/yr - ₹14.8 L/yr |
Myntra
Flipkart
Amazon
Snapdeal
- Home >
- Interviews >
- Red Tag Interview Questions >
- Red Tag Data Scientist Interview Questions
