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Walmart Data Scientist Interview Questions, Process, and Tips
Top Walmart Data Scientist Interview Questions and Answers
- Q1. Technical Question How can you tune the hyper parameters of XGboost algorithm?
- Q2. How can you tune the hyper parameters of XGboost,Random Forest,SVM algorithm?
- Q3. Technical Question How to fit a time series model? State all the steps you would follow.
Walmart Data Scientist Interview Experiences
9 interviews found
Anonymous
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
(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
What people are saying about Walmart
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
Walmart interview questions for designations
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
Get interview-ready with Top Walmart Interview 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
Data Scientist Jobs at Walmart
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
CodingNinjas
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. Technical Question
How can you tune the hyper parameters of 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. Technical Question
Explain the hyper parameters in 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...
(2 Questions)
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. Technical Question
Difference between Ridge and LASSO .
- 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... - Q2. Technical Question
How to fit a time series model? State all the steps you would follow.
- Ans.
Fitting a time series forecasting model requires 5 steps . The steps are explained below :
1) Data preparation : Data preparation is usually the first step where we load all the essential packages and data into a time series object.
2) Time series decomposition : Decomposition basically means deconstructing and visualizing the series into its component parts.
3) Modelling : The actual model building is a simple 2-lines co...
(2 Questions)
Round duration - 50 Minutes
Round difficulty - Medium
This round was based on some basic concepts revolving around Deep Learning .
- Q1. Technical Question
RNN,CNN and difference between these two.
- Ans.
CNN : Convolutional layers . CNNs have unique layers called convolutional layers which separate them from RNNs and other neural networks. Within a convolutional layer, the input is transformed before being passed to the next layer. A CNN transforms the data by using filters.
RNN : Recurrent neural networks are networks that are designed to interpret temporal or sequential information. RNNs use other data points in a seq... - Q2. Technical Question
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. Basic HR Question
Do you know anything 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. Basic HR Question
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
The Data Monk
Interview questions from similar companies
I applied via Naukri.com and was interviewed in Aug 2024. There was 1 interview round.
(2 Questions)
- Q1. Bias and variance with respect to model
- Ans.
Bias and variance are two types of errors that can occur in a model.
Bias refers to the error introduced by approximating a real-world problem, leading to underfitting.
Variance refers to the error introduced by modeling the noise in the training data, leading to overfitting.
Balancing bias and variance is crucial for creating a model that generalizes well to unseen data.
- Q2. Hypotheses test
Skills evaluated in this interview
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Walmart Data Scientist Interview Process
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