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background and some details of take-home some questions are very vague, and really not sure what he want... many questions are open ended for me, bc the take-home is not well-designed from my perspective ... it would be better to ask questions related to statistics or machine learning directly... they don't use any fancy ml techniques, but emphasis the statistics
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Data Scientist

Interviewed at Stitch Fix

3.3★
Mar 31, 2020

background and some details of take-home some questions are very vague, and really not sure what he want... many questions are open ended for me, bc the take-home is not well-designed from my perspective ... it would be better to ask questions related to statistics or machine learning directly... they don't use any fancy ml techniques, but emphasis the statistics

1 coding question : Write a function for sampling from a multimodal distribution. Your inputs are: Keys (i.e. green, red, blue) Weights (i.e. 2, 3, 5.5) N (number of samples drawn from this distribution ( i.e. n=5) Output : list of n keys: Example n = 5: [blue, blue, blue, red, green] Hint: draw a rv of uniform distribution. Calculate the normalized weights for each key. - Machine Learning question : we have a list of items and how many times each item is purchased (range from 10 to 100000 times). For each user the probability of user buying each item is uniform (same across all users). Let’s say we have an item "A" that has been recommended 10 times, has been purchased 10 times. What is the long term probability of being purchased for this item ?
avatar

Data Scientist

Interviewed at Stitch Fix

3.3★
Jul 31, 2020

1 coding question : Write a function for sampling from a multimodal distribution. Your inputs are: Keys (i.e. green, red, blue) Weights (i.e. 2, 3, 5.5) N (number of samples drawn from this distribution ( i.e. n=5) Output : list of n keys: Example n = 5: [blue, blue, blue, red, green] Hint: draw a rv of uniform distribution. Calculate the normalized weights for each key. - Machine Learning question : we have a list of items and how many times each item is purchased (range from 10 to 100000 times). For each user the probability of user buying each item is uniform (same across all users). Let’s say we have an item "A" that has been recommended 10 times, has been purchased 10 times. What is the long term probability of being purchased for this item ?

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