LinkedIn·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

LinkedIn DS interview with a product analytics case about diagnosing a drop in job application volume. One question, open-ended, and they clearly wanted to see structured thinking across multiple layers rather than just one obvious answer.

Questions Asked (1)

A job marketplace sees its daily application count declining week over week. How would you diagnose the root cause and what would you recommend?

Root Cause AnalysisProduct Analytics & MetricsA/B Testing & Experimentation

Author's notes

I jumped straight to funnel analysis which felt right but I spent too long there and almost forgot to check the obvious stuff like whether a recent release broke something.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the metric definition and segmenting the decline by dimensions like user cohort, geography, and platform to isolate where the drop is concentrated. Then form hypotheses about potential causes (e.g., product changes, seasonality, competitive actions) and validate them with data, including checking for instrumentation issues. Finally, recommend actionable next steps, such as an A/B test or further investigation, prioritized by impact and feasibility.

Pro tip: Always rule out data quality issues (e.g., logging bugs, tracking changes) before diving into product or user behavior causes—many 'declines' are actually measurement artifacts. Also, consider external factors like seasonality or macroeconomic trends that could affect the entire marketplace.

1. Clarify the metric and scope

Define what 'daily application count' means (e.g., unique applications, submitted applications) and confirm the time frame and population. Ask if the decline is in absolute numbers or per-user rates, and whether it's consistent across all segments.

2. Segment and localize the decline

Break down the metric by dimensions such as user type (job seeker vs. recruiter), geography, device, job category, and acquisition channel. Identify which segments are driving the overall decline to narrow down potential causes.

3. Generate and prioritize hypotheses

Brainstorm possible causes: product changes (e.g., UI update, algorithm change), seasonality, competitive actions, external events, or data issues. Prioritize hypotheses based on likelihood and potential impact, considering recent events or releases.

4. Validate hypotheses with data

Use data analysis to test each hypothesis: compare pre/post periods, check for correlations with other metrics, and if possible, run a holdback or A/B test. Look for supporting evidence such as changes in user behavior funnels or engagement metrics.

5. Recommend actions and next steps

Based on findings, recommend specific actions: if a bug, fix it; if a product change, consider reverting or iterating; if external, adjust strategy. Propose further experiments or monitoring to confirm and prevent future declines.

Key Points to Mention

Metric definition and data quality checks (e.g., logging, tracking changes)
Segmentation by user cohorts, geography, platform, and job categories
Hypothesis-driven approach: product changes, seasonality, competition, external factors
Use of funnel analysis to identify drop-off points in the application process
A/B testing or holdback groups to establish causality
Prioritization of recommendations based on impact and effort

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