Deloitte Senior Data Scientist Interview Questions, Process, and Tips

Q1. Why you want to join this company?
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Q2. What is your current CTC and expectations
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Q3. What is your notice period
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Q4. Do you have any offers in hand
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Q1. Why you chose this ML model over others
Ans. 
I chose this ML model because of its high accuracy and interpretability.
• The chosen model has shown superior performance in cross-validation compared to other models.

• The model's interpretability allows for easier understanding of feature importance and decision-making processes.

• The chosen model is well-suited for the specific problem domain and dataset characteristics.

• For example, I chose a Random Forest model over a Ne...read more
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Q2. Accuracy and how you know your model does well
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Q1. What is Shap value and plot
Ans. 
Shap values explain individual predictions in machine learning models.
• Shap values quantify the impact of each feature on a model's predictions.

• They help in understanding the importance of different features in the model.

• Shap plots visually represent the impact of features on predictions.

• They can be used to explain black-box models like XGBoost or neural networks.
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Q2. Explain tree based model and hyperparameters
Ans. 
Tree based models use decision trees to make predictions, with hyperparameters controlling the model's behavior.
• Tree based models are a type of machine learning model that uses decision trees to make predictions.

• Hyperparameters are settings that control the behavior of the model, such as the maximum depth of the tree or the minimum number of samples required to split a node.

• Examples of tree based models include Random
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Pharma case study questions

Combination logic on python

Q1. Explain classification
Ans. 
Classification is a machine learning technique used to categorize data into different classes or categories based on past observations.
• Classification involves training a model on labeled data to predict the class of new, unseen data points.

• Common algorithms for classification include logistic regression, decision trees, support vector machines, and k-nearest neighbors.

• Examples of classification tasks include spam email...read more
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Q1. ML, DL, Data Science, Python
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Q1. AI,ML, Data Science, Use cases
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Machine Learning, Metrics

Q1. 1. What is the role of beta value in Logistic regression? 2. What is bias variance trade off? 3. How did you decide on the list of variables that would be used in a model?
Ans. 
Beta value in logistic regression measures the impact of independent variables on the log odds of the dependent variable.
• Beta value indicates the strength and direction of the relationship between the independent variables and the log odds of the dependent variable.

• A positive beta value suggests that as the independent variable increases, the log odds of the dependent variable also increase.

• A negative beta value sugges...read more
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1. You are the data scientist of a digital store. You have to recommend top 10 products to a customer. What variables and techniques will you use to recommend the top 10 products?