Analyze Trends to Diagnose Decline in Job Applications
Quick Overview
Evaluates incident-style diagnosis of a week-over-week decline in job applications. Strong answers verify metric quality, analyze the apply funnel, segment root causes, and recommend mitigations and experiments.
Analyze Trends to Diagnose Decline in Job Applications
Company: LinkedIn
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
A job marketplace notices its daily application count has declined week-over-week.
##### Question
What analyses and actions would you take to diagnose why application count dropped and recommend solutions?
##### Hints
Evaluate funnel metrics, segment by channel/geo, check recent releases, seasonality, competitor moves, and propose experiments to address root causes.
Quick Answer: Evaluates incident-style diagnosis of a week-over-week decline in job applications. Strong answers verify metric quality, analyze the apply funnel, segment root causes, and recommend mitigations and experiments.
Diagnosing a Week-over-Week Drop in Job Applications
A job marketplace observes that daily application count has declined week over week. As the analyst on call, outline the analyses and actions you would take to diagnose the drop, identify likely root causes, and recommend mitigations and experiments.
Constraints & Assumptions
Clarify metric definition, scope, and period alignment before diagnosing causes.
Check data quality and instrumentation first.
Use funnel analysis from traffic to completed applications.
Segment by user, job, channel, geography, device, and apply path.
Clarifying Questions to Ask Guidance
What counts as an application: completed on-site, off-site redirect, ATS submit, or deduped application?
Is the decline absolute applications, applications per seeker, or application conversion rate?
Did any releases, experiments, traffic changes, job inventory changes, or policy changes occur?
Is the drop concentrated by channel, job category, employer, platform, or geography?
Part 1 - Verify and Localize the Drop
Describe initial checks and funnel analysis.
What This Part Should Cover Guidance
Validate event logging, schema changes, time zones, deduping, spam filtering, and source-of-truth totals.
Compare week over week with day-of-week and seasonality controls.
Break the funnel into visits, searches, job views, apply starts, application submits, and confirmations.
Normalize by active seekers and active jobs.
Part 2 - Root Cause Analysis
Identify likely root causes through segmentation and supporting logs.
What This Part Should Cover Guidance
Segment by acquisition channel, geo, device, cohort, job category, employer, apply path, and app version.
Check job supply, ranking/search relevance, apply flow errors, ATS outages, marketing spend, seasonality, and competitor or macro effects.
Review recent releases, experiments, guardrail metrics, and rollback or holdout comparisons.
Part 3 - Mitigation and Experiments
Recommend short-term mitigations and longer-term solutions.
What This Part Should Cover Guidance
Roll back or hotfix obvious instrumentation or product regressions.
Restore traffic, job supply, or apply flow reliability where needed.
Design experiments for ranking, apply-flow, notification, or recommendation fixes.
Define primary and guardrail metrics for recovery.
Follow-up Questions Guidance
What would you do if applications drop but job views are flat?
How would you distinguish job-supply decline from seeker-demand decline?
How would you report uncertainty during an active business incident?