I applied online. The process took 2 weeks. I interviewed at Pocket Gems (San Francisco, CA)
Interview
Contacted by email. Interviewed by phone next week. the interviewer has been very pleasant, and the topic discussed very interesting.
I have been assigned an interesting exercise that involved the planning of an A/B testing, and a ML categorization excercise.
Few days after sending back the exercise I have been called for another interview, where we discussed the exercise and my previous experiences.
Afterwards, I have been invited in San Francisco for a on-site interview. I really enjoyed the whole process, and everyone has been very friendly with me.
Unfortunately i did not received any offer.
Interview questions [1]
Question 1
I have been asked how would I perform some A/B tests.
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Other Data Scientist Interview Reviews for Pocket Gems
I applied online. The process took 2 weeks. I interviewed at Pocket Gems (San Francisco, CA) in Jun 2023
Interview
Phone screen, take home assessment, panel interview. The take home assessment had me analyze the data, then make a recommendation on which segment of the market to target for a new paid feature. The hiring manager said there were no wrong answers, but when asked why I did not move forward, they told me my analysis was incorrect.
Interview questions [1]
Question 1
Which players should we target for our new feature?
I applied online. The process took 1+ week. I interviewed at Pocket Gems (San Francisco, CA) in Aug 2021
Interview
For the first round,
The HR will reach out to you and give you a take-home assignment to complete within 10 calendar days.
You may have to do data analysis from 4 different datasets for the first part and the second part you will have to implement a machine learning model.
Interview questions [1]
Question 1
1. Specify a target group of users for the promotion. Briefly justify your choice using the sample data. (Recommended time: 60 mins for exploratory data analysis, 30 mins for deciding on parameters of the target group.)
2. Build a simple machine learning model in Python to predict a user’s probability of conversion. Please note: we’re not looking for anything complicated here — we’re more interested in how you build a model, which data you choose to train the model, and how you define your target variable. Please do not spend a lot of time obsessing over feature selection, feature engineering, or hyper parameter tuning. (Recommended time: 90 mins.)
It was pretty good. Few statistical questions asked. The interviewer was patient and would lead you when you were not sure about the answer. An assessment was given after the interview.
Interview questions [1]
Question 1
p-value definition/ career goal/ confidence interval