They haven't formulated clear questions yet, but here's what I've gathered: 1. Explaining dimensional modeling. 2. Understanding facts and dimensions and their functionality. 3. Exploring the necessity of surrogate keys. 4. Sharing experiences with unstructured or semi-structured data.
Going through CV and motivation
How do you do optimization modeling
1. Explain PD, LGD, EAD 2. Python Questions: use 'describe' to show the dataframe's info 3. what is p-value 4. confidence interval 5. How to find outlier 6. VaR
Can you write down the formula for the Markov property?
Formule de Black Scholes et EDP Black-Scholes. Re démontrer ces formules.
Q: How do you understand the duty of model risk auditor
Presentation about a specific power industry related topic
Hh
There wasn’t a single question—throughout the process, I had multiple interviews and discussions with different people, covering a wide range of topics.
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