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How to Find a Data Scientist Job Part II

Recently I have been interviewed by around ten companies for a data scientist position. I have written an article about how to find a data scientist job part I and part III, sharing tips about interviewing and job searching. Here I’d like to share some of the most frequently asked questions during the interview. I have also added some answers using hyperlinks.

Technique questions

1. Linear/logistic regression. Focus on the details, such as what’s the assumption about linear regression? What’s the lost function for logistic regression?
2. Ridge Regression and LASSO. What’s the difference between them? What’re the weights would be like?
3. PCA. Please explain how PCA works? The hyperlink directs to a pdf file from the Standford CS229 Machine Learning lecture note, gives a detailed process of PCA.
4. Cross-Validation. How would you use Cross-Validation?
5. Bias and variance. How to find the balance between bias and variance?
6. Ensemble methods. What’s the difference between bagging and boosting? How random forest/XGBoost works? In which algorithm, the depth of decision trees is larger, and why?
7. K-means. Explain how the K-means algorithm works?
8. Learning Rate. What will happen if the learning rate is too big/small?
9. Central limit theorem. What’s your understanding of the Central limit theorem?
10. Covariance, Correlation coefficient, and R². What’s the relationship between them?
11. Bayesian theorem. Typically there will be a question for you to calculate the posterior probability. What’s the equation of the Bayesian theorem and the definition of the prior, posterior?
12. Sampling. Given access to a uniform random number generator over [0, 1], how would you generate a sample from a particular (absolutely continuous for simplicity) distribution?
13. Hypothesis testing. Khan Academy provides a great video series about this topic.
14. Confidence Interval. You can also gain related knowledge from Khan Academy.
15. Cloud computing. Do you have exposure to cloud computing? Can you explain cloud computing to non-tech staff?
16. Error Metric. What’s the difference between MSE and MAE? If the prediction is a constant number, what’s the best choice for MSE and MAE? The link directs to a Coursera notebook. You can find the detailed derivation in “Metrics_video2_constants_for_MSE_and_MAE.ipynb”.

17. Describe a neural network structure that you are familiar with.

Behaviour Questions

1. Tell me about yourself/work/project experience.
2. Why would you like to work as a data scientist?
3. What can you bring to the company if you get the opportunity?
4. What’s motivating you to work for our company?
5. Which principle of our company you like most and why?
6. Why did you leave your last job? What is the part that you enjoy the most/least?
7. Tell me about your experience when you made a mistake.
8. Do you have experience when you have a different opinion with your manager/senior staff, and how did you go through it?
9. Have you ever worked with someone who is hard to cooperate with, and how did you work with them?
10. What do you usually do when you are free? What’s your hobby?
11. What would you do given enough money?
12. Tell me a project you’ve ever done, if you have the chance to do it again, how would you improve it?

Comment if you have interesting questions to be added to this list.