interview questions shared by candidates
The most challenging (and interesting) question was about a strategy for finding signal in a noisy and poorly understood data set. Asking a software engineer a data science question really stretches the mental muscles.
Challenging yes. But not necessarily in a good way. Most software engineers do NOT have the training to properly address this question. It requires a background in "data science" or signal processing. Unless the position interviewed for required these skills/background also, I would say the question was highly inappropriate.
i. Tell me about the supervised machine learning techniques that you know about? ii. If you have a customer and want to decide whether they will “buy today” or “not buy today” and you know 1. where they live, 2. their income, 3. their gender, 4. their profession, how would you define a machine learning algorithm. iii. How does a neural network with one layer and one input and output compare to a logistic regression. iv. For a long sorted list and a short (4 element) sorted list, what algorithm would you use to search the long list for the 4 elements. v. How would the algorithm above scale. vi. Given an unfair coin with the probability of heads not equal to .5. What algorithm could you use to create a list of random 1s and 0s.
The technical challenge was the only remarkable question really and it wasn't too _difficult_ per se, just a bit unclear what level of analysis was called for. Aim for the most thorough and statistically rigorous investigation of the small dataset that you can.