Meta·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Meta data scientist interview with a product-focused question on push notification quality. One question, but it had a lot of moving parts and I felt like I only half-covered what they were looking for.

Questions Asked (1)

A product team wants to improve push notification quality. What data sources would you use to design and deliver better notifications, what metrics would you track to measure success, and how would you quantify any negative impact from sending them?

A/B Testing & ExperimentationProduct Analytics & MetricsProduct Sense & Ideation

Author's notes

I went straight to engagement metrics and kind of forgot to anchor on the data sources first, which I think threw off the flow.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem around user value and business goals, then outline a data-driven approach to identify relevant data sources, define success metrics, and quantify negative impact. Emphasize experimentation (A/B tests) and guardrail metrics to balance engagement with user experience.

Pro tip: Proactively mention the importance of long-term holdout groups and measuring user fatigue to avoid short-term gains at the expense of long-term retention. This shows maturity in experimentation and user-centric thinking.

1. Clarify Objectives and User Segments

Define what 'better notifications' means: increased engagement, relevance, or reduced annoyance. Identify key user segments (e.g., new vs. existing, active vs. dormant) to tailor notifications.

2. Identify Data Sources

List internal data (user behavior, demographics, past notification interactions) and external data (time of day, device type, location) that can inform notification content, timing, and frequency.

3. Define Success and Guardrail Metrics

Choose primary success metrics (e.g., CTR, conversion rate) and guardrail metrics (e.g., opt-out rate, app uninstalls, negative feedback) to monitor unintended consequences.

4. Design Experiments and Quantify Impact

Propose A/B tests to measure the effect of changes on success and guardrail metrics. Use statistical methods to quantify negative impact, such as lift in opt-outs or decrease in DAU.

5. Iterate and Scale

Analyze results, iterate on notification strategies, and scale winning variants while continuously monitoring guardrail metrics to ensure long-term health.

Key Points to Mention

Use of historical notification interaction data (opens, clicks, dismissals) to personalize content and timing.
Incorporate user-level features (e.g., past engagement, preferences) and contextual features (e.g., time zone, device) to optimize delivery.
Define success metrics like CTR, conversion rate, and retention, and guardrail metrics like opt-out rate, uninstall rate, and negative feedback.
Design A/B tests with proper control groups and measure both intended and unintended effects.
Quantify negative impact using metrics such as increase in notification opt-outs, decrease in DAU, or increase in app uninstalls.
Consider long-term effects via holdout groups and measure user fatigue over time.

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