ICBC Senior Statistical Analyst Interview Questions and Answers

Q1. Technical ML QUESTIONS
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Q2. PYTHON QUESTIONS
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Q1. Background check
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Standard online test

Q1. DSA question and SQL
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Q1. LSTM RNN and simple RNN difference
Ans. 
LSTM RNN is a type of RNN that can learn long-term dependencies, while simple RNN struggles with vanishing/exploding gradients.
• LSTM RNN has more complex architecture with memory cells, input, forget, and output gates.

• Simple RNN has a single tanh activation function and suffers from vanishing/exploding gradients.

• LSTM RNN is better at capturing long-term dependencies in sequences.

• Simple RNN is simpler but struggles with
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Q2. What is lasso regression
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Lasso regression is a type of linear regression that uses L1 regularization to prevent overfitting by adding a penalty term to the loss function.
• Lasso regression helps in feature selection by shrinking the coefficients of less important features to zero.

• It is particularly useful when dealing with high-dimensional data where the number of features is much larger than the number of samples.

• The regularization parameter in...read more
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Q1. How to fine tune a llm model
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Fine tuning a LLM model involves adjusting hyperparameters to improve performance.
• Perform grid search or random search to find optimal hyperparameters

• Use cross-validation to evaluate different hyperparameter combinations

• Regularize the model to prevent overfitting

• Adjust learning rate and batch size for better convergence

• Consider using techniques like early stopping to prevent overfitting
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Q1. Normal discussion
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Q1. Salary negotiation
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Q1. 1) Formula for Precision and recall
Ans. 
Precision = TP / (TP + FP), Recall = TP / (TP + FN)
• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class

• Example: If a model predicts 100 positive cases, out of which 80 are actually positive, precision = 80/100, recall = 80/total actual positive cases
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Q2. 2) Difference between R2 and adjusted R2
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R2 measures the proportion of variance explained by the model, while adjusted R2 adjusts for the number of predictors in the model.
• R2 is the proportion of variance in the dependent variable that is predictable from the independent variables.

• Adjusted R2 penalizes the addition of unnecessary predictors in the model, providing a more accurate measure of the model's goodness of fit.

• R2 can increase even when adding irreleva...read more
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Build a simple cat and dog image classifier

Q1. ML related questions
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Q1. Behavioural questions, basically to check if you will be fit in a team
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Questions based on Python & SQL

Case study round was good and interactive

Puzzles
Basic machine learning
Resume project related questions

One case related to insurance
Stress round
Adv ML

Q1. Normal Hr questions
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