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54,372 data scientists interview questions shared by candidates

1. How do you use NN to reduce dimensionality? 2. Can you model time series as a linear regression model? 3. a) Can you use resampling methods like bagging to estimate the max of a population? b) Why is bagging a variance reduction scheme? 4. Why is the use of minibatch to minimize a function computationally more efficient than any other methods? 5.Gambler's ruin problem. 6. Assume that in a time series, some data are missing. How do you handle that? A. average out the existing values. Okay, so you want to average out the existing values, but how do you define the the new time series as a single function? A. Use characteristic or indicator function.
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Data Scientist

Interviewed at Criteo

3.9★
Nov 22, 2018

1. How do you use NN to reduce dimensionality? 2. Can you model time series as a linear regression model? 3. a) Can you use resampling methods like bagging to estimate the max of a population? b) Why is bagging a variance reduction scheme? 4. Why is the use of minibatch to minimize a function computationally more efficient than any other methods? 5.Gambler's ruin problem. 6. Assume that in a time series, some data are missing. How do you handle that? A. average out the existing values. Okay, so you want to average out the existing values, but how do you define the the new time series as a single function? A. Use characteristic or indicator function.

Moderate level questions on the decisions to apply a particular ML algorithm on a dataset, (What they're looking for is a reasoning that factors in everything like inference time, results examinability(XAI or SHAP) and not just what algorithm will be the most accurate on a dataset. )
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Data Scientist II

Interviewed at Fidelity Investments

4.1★
Mar 19, 2023

Moderate level questions on the decisions to apply a particular ML algorithm on a dataset, (What they're looking for is a reasoning that factors in everything like inference time, results examinability(XAI or SHAP) and not just what algorithm will be the most accurate on a dataset. )

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