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Stats: 1. Fundamental laws 1.1. Explain Central Limit Theorem (CLT)? 1.2. Explain Law of Large Number (LLN)? 1.3. What are their differences? How are they beneficial? 2. Statistical Tests 2.1. Tell me the differences/conditions between T-Test vs Z-Test are? When is each of them used? 2.2. When is t-distribution used as opposed to normal distribution? 2.3. How many data points are considered good enough to use each of them? 2.4. How does each distribution look like? (skewness and kurtosis viewpoint) 2.5. Explain p-value in a layman language with a simple example. 2.6. If we run the t-test multiple times, what will happen to the strength of the statistical test? (Bonferroni Correction) 2.7. When is the Chi-Squared test used? How does the distribution look like?
avatar

Senior Data Scientist

Interviewed at Microsoft

4★
Dec 25, 2024

Stats: 1. Fundamental laws 1.1. Explain Central Limit Theorem (CLT)? 1.2. Explain Law of Large Number (LLN)? 1.3. What are their differences? How are they beneficial? 2. Statistical Tests 2.1. Tell me the differences/conditions between T-Test vs Z-Test are? When is each of them used? 2.2. When is t-distribution used as opposed to normal distribution? 2.3. How many data points are considered good enough to use each of them? 2.4. How does each distribution look like? (skewness and kurtosis viewpoint) 2.5. Explain p-value in a layman language with a simple example. 2.6. If we run the t-test multiple times, what will happen to the strength of the statistical test? (Bonferroni Correction) 2.7. When is the Chi-Squared test used? How does the distribution look like?

ML: 1. Linear Regression: 1.1. Explain L1 vs L2? 1.2. How does each affect the coefficients? 1.3. Explain assumptions of linear regression. 1.4. How is each assumption tested? 1.5. If each assumption is violated, what are their remedies? 2. PCA 2.1. Explain PCA. 2.2. Walk me through the algorithm step by step. 2.3. How is the formula constructed? 2.4. What is the relationship between PC1 and PC2? 2.5. How is orthogonality preserved in the mapped feature space? 2.6. How do you run the feature importance in PC-mapped feature space? 3. ML Algorithm 3.1. Explain the ensembling method. 3.2. Explain the differences between XGBoost and Random Forest? 3.3. When is each used? Pros and cons? 3.4. Which one is computationally expensive and why? 3.5. What are the feature selection methodologies? 3.6. Imagine we have a multivariate KPI that most of the features are correlated. Now we are noticing a spike in the KPI, how do you determine which feature has the highest effect on it? (Feature importance analysis for Temporal shock)
avatar

Senior Data Scientist

Interviewed at Microsoft

4★
Dec 25, 2024

ML: 1. Linear Regression: 1.1. Explain L1 vs L2? 1.2. How does each affect the coefficients? 1.3. Explain assumptions of linear regression. 1.4. How is each assumption tested? 1.5. If each assumption is violated, what are their remedies? 2. PCA 2.1. Explain PCA. 2.2. Walk me through the algorithm step by step. 2.3. How is the formula constructed? 2.4. What is the relationship between PC1 and PC2? 2.5. How is orthogonality preserved in the mapped feature space? 2.6. How do you run the feature importance in PC-mapped feature space? 3. ML Algorithm 3.1. Explain the ensembling method. 3.2. Explain the differences between XGBoost and Random Forest? 3.3. When is each used? Pros and cons? 3.4. Which one is computationally expensive and why? 3.5. What are the feature selection methodologies? 3.6. Imagine we have a multivariate KPI that most of the features are correlated. Now we are noticing a spike in the KPI, how do you determine which feature has the highest effect on it? (Feature importance analysis for Temporal shock)

Don't remember much tech questions. Overall, they were at most on medium difficulty. Final round was also technical. Probably a bit more difficult. It was more concentrated on infrastructural aspects - deployment to cloud, quantization etc.
avatar

Senior Data Scientist

Interviewed at PwC

3.7★
Mar 22, 2025

Don't remember much tech questions. Overall, they were at most on medium difficulty. Final round was also technical. Probably a bit more difficult. It was more concentrated on infrastructural aspects - deployment to cloud, quantization etc.

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