How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at OpenAI.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at OpenAI during technical interviews.

Design a free-month experiment | OpenAI Interview Question

Q: Design a free-month experiment

This question evaluates experimental design, causal inference, metric selection, bias mitigation, ROI estimation, and stakeholder communication skills within the Analytics & Experimentation domain for a Data Scientist role.

An online subscription product is considering a promotion that gives eligible new users their first month free instead of charging immediately.

Design an experiment and an analysis plan to estimate the causal impact of the promotion. Your answer should address:

the randomization unit and experiment setup;
the primary decision metric and why simple metrics such as signup rate or retention among signups are not sufficient on their own;
secondary funnel and guardrail metrics;
how to measure short-term and long-term impact when the free month changes both the signup composition and the timing of revenue;
how to estimate ROI when the first month is free and some downstream data are missing or only partially observed;
key pitfalls such as conditioning on post-treatment variables, selection bias, delayed conversion, seasonality, cannibalization, heterogeneous treatment effects, and repeat churn/reactivation;
what assumptions or sensitivity analyses you would use if the dataset is limited;
how you would present the recommendation to a non-technical stakeholder.

You may assume the company can run a randomized controlled experiment on eligible users, but the dataset available to you is imperfect and does not contain every field you would ideally want.

An online subscription product is considering a promotion that gives eligible new users their first month free instead of charging immediately.

Design an experiment and an analysis plan to estimate the causal impact of the promotion. Your answer should address:

the randomization unit and experiment setup;
the primary decision metric and why simple metrics such as signup rate or retention among signups are not sufficient on their own;
secondary funnel and guardrail metrics;
how to measure short-term and long-term impact when the free month changes both the signup composition and the timing of revenue;
how to estimate ROI when the first month is free and some downstream data are missing or only partially observed;
key pitfalls such as conditioning on post-treatment variables, selection bias, delayed conversion, seasonality, cannibalization, heterogeneous treatment effects, and repeat churn/reactivation;
what assumptions or sensitivity analyses you would use if the dataset is limited;
how you would present the recommendation to a non-technical stakeholder.

You may assume the company can run a randomized controlled experiment on eligible users, but the dataset available to you is imperfect and does not contain every field you would ideally want.

Design a free-month experiment

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Design a free-month experiment

Quick Overview

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