experimentation

Shipping a model is itself an experiment, and this question tells apart people who run A/B tests from those who p-hack them. The signal is pre-registering the metric, sizing the test, and holding back from peeking.

Senior DS loops test causal reasoning beyond the randomized A/B test. What interviewers reward is naming the quasi-experimental method that fits and stating the single assumption it lives or dies on. Here is the answer for when randomization is off the table.

A pre-existing gap between your groups means randomization or instrumentation is broken, and the entire experiment is suspect. Strong candidates spot sample-ratio mismatch immediately. Here is the full diagnosis and the disciplined response.

In search and recommendation, A/B tests can be slow and noisy because they pit different users against each other. Interleaving compares two rankers inside the same user's results and spots winners with far less traffic. Here is how.

CUPED can halve the sample size an A/B test requires without touching validity. The signal is explaining why subtracting a pre-experiment covariate reduces variance yet never biases the estimate. Here is the answer.

Standard A/B math assumes one user's treatment does not affect another's outcome. In marketplaces and social networks that assumption breaks and your estimate is biased. The signal is naming SUTVA and the right randomization unit. Here is the answer.

When every team evaluates prompts in one-off notebooks, an org ships regressions and argues about vibes. A shared eval platform turns quality into something measurable and comparable. Here is what it must provide.

Running experiments on LLM features is tough because outputs are open-ended and quality is fuzzy. See how to assign traffic, choose metrics beyond engagement, tame variance from non-determinism, and dodge the traps that let a winning variant lose once it ships.

A dependable experiment platform goes well beyond splitting traffic in half. What matters is stable assignment, exposure logging, statistical discipline, and guardrails that hold up against peeking and sample-ratio mismatch. Here is the design.

This question rewards intellectual honesty and a data-driven mindset over a polished win. The signal is running a real experiment, accepting a result you did not want, and acting on it. Here is the arc that connects at experiment-heavy cultures.