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Sr Data Scientist Interview Questions

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3,429 sr data scientist interview questions shared by candidates

1. What is the difference between Batch Normalization and Layer Normalization? How do they impact training? 2. Explain the concept of attention mechanism in neural networks. How is it used in transformer models? 3. What are GANs (Generative Adversarial Networks), and how do they work? 4. Describe the concept of transfer learning. When and how would you use it? 5. What is the difference between Markov Chains and Hidden Markov Models? Provide examples of their applications. 6. How does the backpropagation algorithm work in neural networks? 7. What are the key differences between L1 and L2 regularization? In which scenarios would you use each? 8. Explain the working of a convolutional neural network (CNN). What are its primary components? 9. How does a recurrent neural network (RNN) handle sequential data? Explain vanishing and exploding gradients in RNNs. 10. Describe the process of gradient descent. What are some variations, and when would you use them?
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Senior Data Scientist

Interviewed at Valiance Solutions

3.5★
Aug 7, 2024

1. What is the difference between Batch Normalization and Layer Normalization? How do they impact training? 2. Explain the concept of attention mechanism in neural networks. How is it used in transformer models? 3. What are GANs (Generative Adversarial Networks), and how do they work? 4. Describe the concept of transfer learning. When and how would you use it? 5. What is the difference between Markov Chains and Hidden Markov Models? Provide examples of their applications. 6. How does the backpropagation algorithm work in neural networks? 7. What are the key differences between L1 and L2 regularization? In which scenarios would you use each? 8. Explain the working of a convolutional neural network (CNN). What are its primary components? 9. How does a recurrent neural network (RNN) handle sequential data? Explain vanishing and exploding gradients in RNNs. 10. Describe the process of gradient descent. What are some variations, and when would you use them?

The data was a mix of anonymised categorical and numerical features of (I think) cars that were shown to users that ended up being booked. There was an open ended requirement of coming up with data analysis graphs, a simple machine learning model, a discussion on a possible product that could be built on it, and a discussion on either the price sensitivity of users, or the importance of price as a variable that's factored in by users when making a purchase.
avatar

Senior Data Scientist

Interviewed at CarTrawler

3★
Mar 20, 2018

The data was a mix of anonymised categorical and numerical features of (I think) cars that were shown to users that ended up being booked. There was an open ended requirement of coming up with data analysis graphs, a simple machine learning model, a discussion on a possible product that could be built on it, and a discussion on either the price sensitivity of users, or the importance of price as a variable that's factored in by users when making a purchase.

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