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Lowe's Data Scientist Interview Questions and Answers
Lowe's Data Scientist Interview Experiences
1 interview found
Anonymous
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
Data Scientist Jobs at Lowe's
Interview questions from similar companies
Selected
I applied via Referral and was interviewed before Aug 2022. There were 3 interview rounds.
(1 Question)
- Q1. Heavy SQL solutions
Retail case study, with soft skills is required for this round
Interview Preparation Tips
I applied via Naukri.com and was interviewed in Nov 2021. There were 2 interview rounds.
(1 Question)
- Q1. Round one was a combination of a technical round and a discussion on previous work experience. Questions - Previous work experience, Basic SQL questions, Basic Python questions, Basic Tableau question...
(1 Question)
- Q1. This was a culture fit round. This was a one on one discussion with the manager. Offer letter was rolled out after this round.
Interview Preparation Tips
Selected
I applied via Approached by Company and was interviewed in Jul 2024. There was 1 interview round.
Python and sql based questions
(2 Questions)
- Q1. Intermediate level SQL questions using joins and case when
- Q2. String manipulation advanced level question in python
Array-based question
(2 Questions)
- Q1. Explain Xgboots
- Ans.
XGBoost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
XGBoost stands for eXtreme Gradient Boosting.
It is an implementation of gradient boosted decision trees designed for speed and performance.
XGBoost is widely used in machine learning competitions and real-world applications.
It can handle missing data, regularization, and parallel processing efficiently.
XGBoost ...
- Q2. Explain random forest
- Ans.
Random forest is an ensemble learning method that builds multiple decision trees and combines their predictions.
Random forest is a type of ensemble learning method.
It builds multiple decision trees during training.
Each tree in the forest makes a prediction, and the final prediction is the average or majority vote of all trees.
Random forest is used for classification and regression tasks.
It helps reduce overfitting and ...
(2 Questions)
- Q1. Explain about your project
- Q2. Explain the sequence to sequence models and transformers
- Ans.
Sequence to sequence models are used in natural language processing to convert input sequences into output sequences.
Sequence to sequence models are commonly used in machine translation tasks, where the input is a sentence in one language and the output is the translated sentence in another language.
Transformers are a type of sequence to sequence model that use self-attention mechanisms to weigh the importance of diffe...
Skills evaluated in this interview
Aishwarya Shukla
(1 Question)
- Q1. Past experience
Selected
I applied via LinkedIn and was interviewed before Jan 2024. There were 4 interview rounds.
Case Study was related to customer propensity to buy.
(2 Questions)
- Q1. Linear Regression Assumptions
- Ans.
Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
Assumption of linearity: The relationship between the independent and dependent variables is linear.
Assumption of independence: The residuals are independent of each other.
Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.
Assumption of normality: The resid...
- Q2. ML Algorithms and Evaluation Metrics.
(1 Question)
- Q1. What is VIF(variance inflation factor)
- Ans.
VIF is a measure of multicollinearity in regression analysis, indicating how much the variance of an estimated regression coefficient is increased due to collinearity.
VIF values greater than 10 indicate high multicollinearity
VIF is calculated for each predictor variable in a regression model
VIF is calculated as 1 / (1 - R^2) where R^2 is the coefficient of determination from regressing a predictor on all other predicto
(1 Question)
- Q1. Why do you want to join?
- Ans.
I am impressed by your company's innovative projects and collaborative work culture.
I admire the company's commitment to cutting-edge technology and data-driven solutions.
I am excited about the opportunity to work with a talented team of data scientists and researchers.
Your company's reputation for fostering a collaborative and inclusive work environment is appealing to me.
Interview Preparation Tips
I appeared for an interview 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
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