LinkedIn·Data Scientist·Technical Phone Screen
Jun 2026
LinkedIn data science technical screen, pretty much all stats fundamentals. Six questions back to back covering queuing theory, height distributions, social network degree distributions, and regularization. Nothing crazy hard but a couple of them required more careful thinking than I expected.
- A bank has 5 tellers. System A has all 5 sharing one common queue. System B gives each teller their own separate queue. As a customer, which would you prefer and why? Compare the two on expected wait time, variance of wait time, and fairness.
- Describe the distribution of adult male heights in the US, and separately, the distribution of adult female heights.
- If you pool men and women together and ignore sex entirely, what does the combined height distribution look like?
- On a social network like LinkedIn, what does the distribution of number of connections per user look like? Is it symmetric, left-skewed, or right-skewed?
- For that connections distribution, how do the mean, median, and mode compare? And roughly what factors would determine the scale of the mean?
- Are L1-regularized (lasso) and L2-regularized (ridge) estimators biased? Why or why not?
“This was the most interesting question of the screen.” The rest of the author's notes on Data Scientist interview at LinkedIn, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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