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Could you describe some of the machine learning architectures that you've worked with thus far in your career?

How do you use cross-validation to evaluate the performance of a machine learning model?

Which aspects of the Data Scientist role at Asana appeals to you most?

What can you tell me about the efficiency of SVM training in terms of time complexity? Could you explain the time efficiency of inference with a trained SVM model? Could you describe how kernel selection (Linear vs RBF) affects the time complexity of SVMs?

Tell us about how Asana aligns with your longer term plans

Let's say for sample size n, the margin of error is How many more samples do we need to make the margin of error to 0.3?

How does an ROC curve assist in determining the threshold for a classification model? Can you discuss the trade-offs between bagging and boosting techniques?

Which loss functions are you experienced with within the context of machine learning models?

Please share with me a time you failed at work.

What do the terms parametric and non-parametric mean in statistics, and how do these methodologies differ in their statistical modeling techniques?