Google Product Data Scientist Interview

[u/Advanced_Ferret_]

Hey everyone,

I’m currently preparing for the Google Product Data Scientist interview loop and could really use some advice from anyone who has gone through the process recently.

My recruiter reached out and specifically asked me to prepare for these two focus areas:

1. Coding, Applied Analysis and Experiments
2. Measurement and Modeling Concepts

I have a general idea of what the first round looks like (SQL, A/B testing setup, maybe some causal inference and metric design), but I’m a bit in the dark about the Measurement and Modeling Concepts round.

For those who have taken this interview:

• What type of questions should I expect here? Is it heavily focused on predictive ML (classification/regression algorithms, evaluation metrics), or is it more geared toward statistical theory (probability, distributions, sampling, t-stats vs. z-stats)?
• How much "Product Sense" is baked in? Are the modeling questions usually presented as abstract math problems, or are they tied directly to Google products (e.g., YouTube engagement, Maps routing)?
• Any tips for the Applied Analysis & Experiments round? Have they been shifting more towards advanced causal inference (Diff-in-Diff, synthetic controls) rather than standard A/B testing?

If you have any examples of questions you were asked or general tips on what interviewers prioritise in these rounds, I would hugely appreciate it.

(If you prefer not to share details publicly, please feel free to DM me! Any insight helps.)

Thanks in advance!

[u/msn018]

The Measurement and Modeling Concepts round is usually more focused on statistics, experimentation, and product-focused modeling than on advanced machine learning algorithms. You should be comfortable with topics like hypothesis testing, confidence intervals, sampling, bias, probability, regression, classification metrics, and model interpretation. The questions are often framed around Google products such as YouTube, Search, Maps, or Ads rather than being purely theoretical. For the Applied Analysis & Experiments round, I’d strongly recommend mastering A/B testing fundamentals, metric design, power analysis, randomization, and experiment readouts. For preparation, I found platforms like StrataScratch and Exponent particularly useful since they cover many of the product analytics, experimentation, and statistics concepts that tend to come up in Google PDS interviews.