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.
EasyBehavioral
0 practiced
Tell me about a time when your analysis produced an insight that changed a product or business decision. Describe the business question, the dataset and tools you used, the analysis steps from hypothesis to result, how you validated the finding, how you communicated it, and the measurable outcome after the change.
EasyTechnical
0 practiced
Using Python and pandas, write a function that takes a DataFrame df with columns ['user_id','event_date','converted'] and returns a DataFrame of daily conversion rate and its 7-day centered rolling average. Assume event_date is a datetime and there may be missing dates for some users. Briefly explain edge handling.
HardTechnical
0 practiced
Given a credit-scoring model, design an analysis to detect algorithmic bias across protected groups. Specify fairness metrics to compute (statistical parity, equalized odds, calibration), how to test significance of disparities, and propose mitigation strategies (preprocessing, in-processing, post-processing) with trade-offs.
MediumTechnical
0 practiced
Explain uplift (heterogeneous treatment effect) modeling and provide a concrete business example where it is preferable to standard A/B testing or response modeling. Briefly outline the modeling approaches and evaluation metrics specific to uplift.
HardTechnical
0 practiced
You must forecast demand where event timestamps are irregular (e.g., call logs) and some intervals are missing. Explain preprocessing options, model choices (state-space models, continuous-time processes, Prophet with regressors, RNN/LSTM with time-gap features), and how you'd validate forecast accuracy and uncertainty for decisions like staffing.
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