Roblox·Data Scientist·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026Remote

Summary

Roblox DS interview with a product analytics case focused on homepage tab experimentation. Pretty much one meaty question that covered a lot of ground: metric selection, experiment design, and statistical thinking all rolled into one.

Questions Asked (1)

Roblox is replacing an existing homepage tab with a new one. How would you evaluate the business impact of this change? Walk through your primary and guardrail metrics, your hypotheses, and design a full experiment plan including sample size, duration, segmentation, and success criteria.

A/B Testing & ExperimentationProduct Analytics & Metrics

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal of the new homepage tab and how it fits into Roblox's ecosystem. Then outline a structured experiment plan that includes primary and guardrail metrics, hypotheses, sample size calculation, duration, segmentation, and success criteria. Emphasize the importance of balancing user experience with business metrics.

Pro tip: Consider the network effects and social aspects unique to Roblox; changes to the homepage can impact engagement across the platform, so include metrics that capture cross-side effects and long-term retention.

1. Define Objectives and Hypotheses

Clarify the business goal of the new tab (e.g., increase engagement, retention, or monetization) and formulate clear, testable hypotheses about its impact.

2. Select Metrics

Choose primary metrics that directly measure success (e.g., click-through rate, time spent, DAU) and guardrail metrics to ensure no negative impact (e.g., churn, crash rates, user reports).

3. Design Experiment

Determine sample size using power analysis, set experiment duration to capture weekly patterns, and define randomization unit (e.g., user-level) and segmentation (e.g., new vs. existing users).

4. Analyze and Decide

Predefine success criteria (e.g., statistically significant improvement in primary metric without degradation in guardrails) and analyze results, considering novelty effects and long-term impact.

Key Points to Mention

Primary metrics: engagement (e.g., DAU, session length), monetization (e.g., revenue per user), and retention.
Guardrail metrics: user churn, crash rates, support tickets, and negative feedback.
Hypotheses: e.g., new tab increases discoverability leading to higher engagement, but may disrupt habitual behavior causing short-term dip.
Sample size calculation: based on minimum detectable effect, power (80%), significance level (5%), and variance of metrics.
Duration: at least 1-2 weeks to account for weekly seasonality and novelty effects.
Segmentation: analyze by user tenure, platform (mobile/desktop/console), and geography to detect heterogeneous effects.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.