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Sr Data Scientist Interview Questions

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3,392 sr data scientist interview questions shared by candidates

1st user interview: what you would do when given noisy data (what preprocessing steps you would carry out), variance, covariance, correlation, recall, precision, F1-score, examples of when you need precision over recall and vice versa, what do you know about A/B test (what steps you should do), and some study cases. 2nd user interview: mainly at a higher level than in the previous interview. He also asked me if I had an experience where I wrote a custom loss function (could be from a paper) of a deep learning model.
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Senior Data Scientist

Interviewed at Tokopedia

4.2★
Dec 3, 2022

1st user interview: what you would do when given noisy data (what preprocessing steps you would carry out), variance, covariance, correlation, recall, precision, F1-score, examples of when you need precision over recall and vice versa, what do you know about A/B test (what steps you should do), and some study cases. 2nd user interview: mainly at a higher level than in the previous interview. He also asked me if I had an experience where I wrote a custom loss function (could be from a paper) of a deep learning model.

Assessement: Questions regarding sql, ML, stats , and 1 coding for ML and one for postsql 1st interview: Basics of ML, stats, python , sql like overfitting, vif, hyperparameter tuning, ar and ma model of time series and difference among them, gradient descent 2nd intervew: One aptitude, Architechture of LSTM, working of tanh gate, why data should be stationary for time series, one solving based on gradient descent, dropout.
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Senior Associate Data Scientist

Interviewed at Affine

3.5★
Jul 12, 2024

Assessement: Questions regarding sql, ML, stats , and 1 coding for ML and one for postsql 1st interview: Basics of ML, stats, python , sql like overfitting, vif, hyperparameter tuning, ar and ma model of time series and difference among them, gradient descent 2nd intervew: One aptitude, Architechture of LSTM, working of tanh gate, why data should be stationary for time series, one solving based on gradient descent, dropout.

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