Amazon·Data Scientist·Technical Phone Screen
May 2026
Amazon data scientist interview focused almost entirely on ML fundamentals through the lens of a churn prediction scenario. Four questions, all technical, and they kept the scenario consistent throughout which I wasn't expecting.
- In a churn prediction problem with messy real-world data, how would you handle missing values in your training set and why?
- For this churn prediction scenario, which ML algorithm would you pick and what's your reasoning?
- Walk me through how Random Forest works, specifically the voting mechanism, how features are sampled at each split, and how tree depth is controlled.
- How do you define overfitting and underfitting, and what methods do you use to detect and fix each?
“I went with imputation pretty quickly, mean for numerical stuff, mode for categoricals, but they pushed back and asked when that might actually hurt you.” The rest of the author's notes on Data Scientist interview at Amazon, 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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