Analytical Background Questions
The candidate's analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
HardTechnical
0 practiced
During validation you find model performance is suspiciously high. Describe an approach to detect whether data leakage (target leakage, timestamp leakage, or improper joins) is inflating metrics, how to locate leaking features, and how to remediate leakage both in training and in production feature pipelines.
MediumTechnical
0 practiced
You receive a dataset for model training with many missing values across columns. Describe your end-to-end analytic process to (1) quantify missingness patterns, (2) decide if data are MCAR/MAR/MNAR, (3) choose imputation or modeling strategies (e.g., drop, mean/median, KNN, MICE, model-based), and (4) evaluate whether imputation changed downstream model performance. Mention tools and code snippets or libraries you would use.
EasyTechnical
0 practiced
Explain precision, recall, specificity, F1 score, and ROC-AUC for a binary classifier. For an AI use-case like spam detection or fraud detection, discuss when each metric is most appropriate, trade-offs between precision and recall, and how threshold selection impacts production behavior (false positives vs false negatives).
HardTechnical
0 practiced
Explain Simpson's paradox with a concrete analytics example where two product segments both improved conversion but the aggregated conversion decreased. Show how to detect Simpson's paradox in your data, what diagnostics to run, and how to present the correct stratified interpretation to stakeholders to avoid misleading aggregated conclusions.
EasyTechnical
0 practiced
In Python (pandas), given a DataFrame 'events' with columns ['user_id', 'event_type', 'timestamp'] where event_type in ('view','purchase'), write a function to compute daily conversion rate (purchases/views). Requirements: normalize timestamps to a given timezone/date, remove duplicate purchases per user-day, handle nulls, and return a DataFrame ['date','views','purchases','conversion_rate']. Discuss complexity and memory considerations for large datasets.
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