Google·Data Scientist·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Google DS interview with a meaty growth problem centered on Workspace Chat. The whole thing was essentially one long case question about driving MAU growth, and they wanted you to go pretty deep on the experimental side of things.

Questions Asked (1)

Google Workspace Chat has low adoption. How would you build a plan to grow monthly active users, including which metrics to track, what hypotheses you'd form, how you'd structure an A/B testing roadmap, and what success looks like?

A/B Testing & ExperimentationProduct Analytics & MetricsProduct Strategy

Author's notes

This one sprawled in a way I wasn't ready for.

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AI HintsAI Generated

Suggested Approach

Start by defining the problem and clarifying the goal: increase monthly active users (MAU) for Google Workspace Chat. Then, outline a structured plan that includes metric selection, hypothesis generation, experiment design, and success criteria. Emphasize a data-driven, iterative approach with a focus on actionable insights.

Pro tip: Show that you understand the difference between correlation and causation by discussing how you would isolate the impact of your changes from external factors. Also, mention the importance of guardrail metrics to ensure that growth doesn't come at the expense of user experience or other key metrics.

1. Define the problem and metrics

Clarify what 'low adoption' means and identify the key metrics to track, such as MAU, DAU/MAU ratio, retention, and engagement metrics. Consider segmenting by user type or platform.

2. Form hypotheses

Generate hypotheses for why adoption is low, based on data and user research. For example, lack of awareness, poor onboarding, missing features, or competition from other tools.

3. Prioritize and design experiments

Prioritize hypotheses based on potential impact and ease of implementation. Design A/B tests or other experiments to test each hypothesis, ensuring proper randomization and sample size.

4. Structure the A/B testing roadmap

Create a roadmap that sequences experiments logically, starting with high-impact, low-effort changes. Include a plan for iterating based on results and scaling successful experiments.

5. Define success and iterate

Specify what success looks like in terms of metric improvements and statistical significance. Plan for continuous monitoring and iteration to sustain growth.

Key Points to Mention

North Star metric: Monthly Active Users (MAU) and supporting metrics like DAU/MAU, retention, and engagement.
Hypothesis generation: Use qualitative and quantitative data to form testable hypotheses.
A/B testing best practices: Randomization, control groups, sample size calculation, and avoiding common pitfalls like peeking.
Prioritization frameworks: Use ICE (Impact, Confidence, Ease) or RICE (Reach, Impact, Confidence, Effort) to prioritize experiments.
Guardrail metrics: Ensure that growth initiatives don't negatively impact other key metrics like user satisfaction or performance.
Success criteria: Define clear, measurable goals for each experiment and overall program, with statistical significance and practical significance.

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