Sainsburys Data Scientist Interview Questions and Answers

Business Related case study, signed NDA

Q1. Count number of Parameters in BERT
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Q2. 3 sum array problem
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Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Easy topics arrays, sequence sum.

Q1. How do you define model Gini?
Ans. 
Model Gini is a measure of statistical dispersion used to evaluate the performance of classification models.
• Model Gini is calculated as twice the area between the ROC curve and the diagonal line (random model).

• It ranges from 0 (worst model) to 1 (best model), with higher values indicating better model performance.

• A Gini coefficient of 0.5 indicates a model that is no better than random guessing.

• Commonly used in credit
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Q2. How to you train XG boost model
Ans. 
XGBoost model is trained by specifying parameters, splitting data into training and validation sets, fitting the model, and tuning hyperparameters.
• Specify parameters for XGBoost model such as learning rate, max depth, and number of trees

• Split data into training and validation sets using train_test_split function

• Fit the XGBoost model on training data using fit method

• Tune hyperparameters using techniques like grid search
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Q1. Transformer models.
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Related to Mathematical intuition in Deep learning.

Q1. Tell me about yourself
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Q2. Questions from projects which was mentioned on resume
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Q1. Fitment based questions
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Q2. Explain a project
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Collected and cleaned customer data including usage patterns and demographics

• Used classification algorithms like Random Forest and Logistic Regression to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall
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Math, English, reasoning

Q1. Asked me about expected salary
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Data Science MCQ questions

Building a baseline ML model with EDA etc.

Q1. How do you analyse outliers?
Ans. 
Outliers can be analyzed using statistical methods like Z-score, IQR, or visualization techniques like box plots.
• Calculate Z-score and identify data points with Z-score greater than a certain threshold as outliers.

• Use Interquartile Range (IQR) to detect outliers by identifying data points outside 1.5 * IQR range.

• Visualize data using box plots to identify any data points that fall outside the whiskers.

• Consider domain kn...read more
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Q2. Tell me about a time when what you found did not match expectation with what you thought analysing the dataset.
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Q1. Why Unilever now?
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Q2. Where do you see yourself in 5 years?
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Q1. 1. diff between acf pacf
Ans. 
ACF measures the linear relationship between an observation and its lagged values. PACF measures the direct relationship.
• ACF (Autocorrelation Function) measures the correlation between an observation and its lagged values.

• PACF (Partial Autocorrelation Function) measures the correlation between an observation and its lagged values, while removing the indirect effects of intermediate lags.

• ACF is used to identify the orde...read more
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