These are mostly open-ended questions, to assess the technical horizontal knowledge of a senior candidate at a high level.

1. What is the biggest data set that you processed, and how did you process it, what were the results?
2. Tell me two success stories about your analytic or computer science projects? How was lift (or success) measured?
3. What is: lift, KPI, robustness, model fitting, design of experiments, 80/20 rule?
4. What is: collaborative filtering, n-grams, map reduce, cosine distance?
5. How to optimize a web crawler to run much faster, extract better information, and better summarize data to produce cleaner databases?
6. How would you come up with a solution to identify plagiarism?
7. How to detect individual paid accounts shared by multiple users?
8. Should click data be handled in real time? Why? In which contexts?
9. What is better: good data or good models? And how do you define "good"? Is there a universal good model? Are there any models that are definitely not so good?
10. What is probabilistic merging (AKA fuzzy merging)? Is it easier to handle with SQL or other languages? Which languages would you choose for semi-structured text data reconciliation? 

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