Goldman Sachs Data Scientist Interview Questions
The questions to prepare for a Goldman Sachs Data Scientist interview. Questions from real interview reports rank first. Updated daily.
Explain practical SQL techniques to preserve data integrity when combining multiple data sources.
Goldman SachsExplain practical SQL methods for analyzing large datasets, including filtering, aggregation, sampling, and performance-aware query design.
Goldman SachsCompute daily active users and a 7-day rolling average using a CTE, distinct counts, and window functions.
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Meta ITIdentify Goldman Sachs customers with purchases in consecutive calendar months using month normalization and LAG.
Goldman SachsUse joins, aggregation, and ROW_NUMBER to find the top three Goldman Sachs products by revenue in each category.
Goldman SachsExplain a practical framework for feature engineering, from raw data review to validation of feature impact on held-out data.
Goldman SachsExplain how bagging and boosting differ, and identify a representative algorithm for each ensemble method.
Goldman SachsDefine the right metrics to judge whether a new product feature is successful.
Goldman SachsDesign an experiment for a new trading signal or workflow change, including metrics, power, randomization, and launch criteria.
Goldman SachsExplain your approach to model evaluation, including how you choose and interpret metrics for different ML problems.
Goldman SachsTests understanding of hypothesis testing and how it informs data-driven decisions.
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