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In machine learning, dealing with covariate imbalance is an important consideration. Can you explain some approaches or techniques that can be used to address covariate imbalance in a dataset? How do these approaches help in improving the performance of machine learning models?

Tell me about a conflict you managed.

Let's say we have a coin. How would you determine the p value needed for the coin to be fair?

What do you hope to achieve in the next five years?

Can you discuss the convergence of stochastic gradient descent (SGD) and its advantages over gradient descent?

How do you go about generating 1000 data points with a normal distribution given mean=x and variance=y, and what summary statistics and visualization would you use to describe the dataset?

How would you go about writing a function that can find words in a list that are anagrams of another word in that same list?

What would be your ideal team to join in FactSet

How does the convergence mechanism of SGD provide benefits over that of regular gradient descent?

Please share an experience when you took a risk.