Jio Data Scientist Interview Questions and Answers

Round duration - 60 minutes
Round difficulty - Easy

Technical Interview round with questions on ML mainly.

Q1. What are the assumptions of a linear regression model?
Ans. 
Assumptions of a linear regression model include linearity, independence, homoscedasticity, and normality.
• Linearity: The relationship between the independent and dependent variables is linear.

• Independence: The residuals are independent of each other.

• Homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Normality: The residuals are normally distributed.

• No multicolline...read more
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Q2. What problems does multicollinearity cause in regression analysis?
Ans. 
Multicollinearity in regression analysis causes issues like inflated standard errors, unstable coefficients, and difficulty in interpreting the importance of predictors.
• Multicollinearity leads to inflated standard errors, making it difficult to determine the significance of predictors.

• It causes unstable coefficients, as small changes in the data can result in large changes in the coefficients.

• Interpreting the importanc...read more
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Q3. What are the different measures used to evaluate the performance of a classification model?
Ans. 
Different measures used to evaluate classification model performance
• Accuracy: Overall correctness of the model's predictions

• Precision: Proportion of true positive predictions among all positive predictions

• Recall: Proportion of true positive predictions among all actual positives

• F1 Score: Harmonic mean of precision and recall

• Confusion Matrix: Summarizes the performance of a classification model
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Q4. What are the disadvantages of logistic regression?
Ans. 
Disadvantages of logistic regression
• Assumes linearity between independent variables and log odds of the dependent variable

• Prone to overfitting with large number of features

• Not suitable for complex relationships or non-linear data

• Can't handle missing values well
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Q1. All technical questions related to profile
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Q1. Data science questions
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Q1. Current role and experience related
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Q1. Technical questions
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Q2. Data science questions by onshore team
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Q1. Salary joining date
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Q2. Expected salary joining date
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Q1. ML questions and deep learning
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Q2. Project in your cv
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Used Python and scikit-learn for data preprocessing and model building

• Performed feature engineering to improve model performance

• Evaluated model using metrics like accuracy, precision, and recall
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Basic aptitude ques were asked

Q1. Projects discussion
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Q2. Probability questions
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Q1. Why Ericsson and 5 year plan
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Q2. Your strength and weakness
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