causaLens Data Scientist Interview Questions
The questions to prepare for a causaLens Data Scientist interview. Questions from real interview reports rank first. Updated daily.
Tests prioritization under competing research demands, stakeholder communication, and ownership of deadlines.
Explain window functions and calculate a rolling average for time-series measurements.
Compare Ridge and Lasso mathematically, then select between dense shrinkage and sparse feature selection based on data structure.
Define a meaningful engagement metric for a retail analytics mobile application and explain how to validate it.
Describe an A/B test you ran, what question it answered, how you measured success, and what you learned from the results.
Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.
Explain how to detect, classify, and safely handle missing values in PostgreSQL production datasets.
Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.
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Calculate each user's daily and cumulative Reddit comment count, including dates with no activity.
RedditCalculate a rolling average of order values per Zomato user using a window function.
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