Questions assez classiques, très orientées vers ce que souhaite faire le candidat
Senior Data Analyst Interview Questions
2,914 senior data analyst interview questions shared by candidates
Project Based questions and understanding
What’s the mathematics behind logistic regression, how to evaluate multiple classification output, explain every evaluation metric of GLM and OLS method, like what’s Pseudo R-square, F statistic, AIC, BIC, Durbin Watson, Jarque bera and again 2 live python coding questions and even after producing the required output he asked to further simply. These questions were asked in 2nd round, I was stunned with the level of questions for a senior data analyst role, I can understand if these were asked for senior data scientist position, my bad luck, failed in 2nd round
Knowledge potential for the role
Questions related to the case study and SQL intermediate to advanced questions, some PowerBI stuff.
behavior questions and hands-on tests
Interview was behavorial questions around the position and technical questions based on the past experience. After 2-3 weeks of silence, I get the message from HM that position has been closed due to internal changes. I am super disappointed for giving those hours of interview and getting such unprofessional response. STOP WASTING CANDIDATE's TIME
Specific is growth related topics
**Business Case & Additional Business Questions:** 1. What additional data features/data columns/data series could have been useful for forecasting and predictive modeling? 2. What are things that can give you a better overall estimate of the order value? 3. How to ensure experimenting with adding more orders per courier (batching) with the intention of reducing costs doesn’t harm customer satisfaction? 4. Regarding the A/B test used to experiment with batching, how would you deal with the following segmentation challenge: I'm dividing users randomly, some of the final users of the same batch might end up in the control and others in the treatment group. So your segmentation between control and treatment wouldn’t be really working. 5. Let’s say to deal with the issue from the last question you segment your users into north and south. But then which other issues do you face? 6. A senior decision maker comes and asks you “Okay, you guys at the data department are quite smart, but how confident are you on your prediction model’s forecast?” I understand you used metrics to evaluate model performance and that the model is usually off by 10%, but how confident are you in your model’s forecast? How would you arrive at this metric. --- **SQL Questions:** 1. How to pivot the structure of a table for better aggregation? 2. What qualities make a good SQL Analyst?
Case question: From this data, where would you focus product efforts, if the goal was to maximize revenue per client?
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