Google·Data Scientist·Technical Phone Screen
Jun 2026
Google DS interview that was basically a long spatial statistics case study. The whole thing revolved around one big open-ended problem about bird species distribution, but it had enough sub-parts that it felt like four separate questions back to back. Harder than I expected for a DS role.
- How would you operationally define spatial segregation between bird species, and what quantitative metric would you use to measure it?
- What is the correct null hypothesis for a segregation test on this observational dataset, and why is a naive 'completely random placement' null inappropriate here?
- How would you obtain a p-value for your segregation metric, and how do you handle the multiple comparisons problem across species pairs?
- Walk through how your approach handles uneven species abundance, habitat confounding, spatial scale differences, sampling bias, and rare species with very few observations.
- If the coordinates are plot centroids rather than true point locations, which of your proposed methods break down and which still work?
- If two species remain negatively associated even after controlling for habitat and survey effort, what additional evidence would you need before concluding there is active avoidance rather than an unmeasured environmental gradient?
- The dataset spans multiple seasons. How would you incorporate observation timestamps so that seasonal species turnover doesn't get misread as spatial segregation?
- If the ecology team wants a single summary number representing overall forest segregation, how would you responsibly aggregate results that vary across spatial scales and species pairs?
“I went straight to nearest-neighbor stats, which felt right.” The rest of the author's notes on Data Scientist interview at Google, 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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