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How do you tackle covariate imbalance in machine learning data, and how does it improve model results?

If you were to get the Data Scientist job, how would you improve Zoox?

Could you explain the importance of training and loss graphs for neural networks? How do various loss functions impact the shape of the loss graph, and what are some commonly known ones? What is the contrast between the training and loss graphs, and how do they assist in optimizing and maximizing model performance?

Where do you find yourself in terms of balancing your current workload?

Describe a project you're really proud of.

What are some techniques used to reduce dimensionality, and how are they applied in Machine Learning?

Let's imagine we have a list of (date, duration) tuples. How would you filter by a specific time frame and then calculate summary stats like the average duration and rolling sum?

How would you go about coding a functional hash table in Python?

In your experience, what are the best practices for minimizing residuals in statistical modelling?

Where so you see yourself in next 5 years?