Jobright.ai / Agent Analytics
Platform analytics dashboard modeled on Jobright's AI job-matching workflow. Match score distribution, agent recommendation accuracy, user funnel from discovery to application, and A/B test results on AI agent suggestions — all on synthetic data.
RoleAnalyst, work sample
Duration4 days
Year2026
StatusShipped
The brief.
Jobright.ai is an AI-native job matching platform. Their data team measures agent performance, match quality, and user conversion — a stack I'm uniquely positioned for as both a builder and an active user of the platform during my own job search.
The work.
USER_FUNNEL / DISCOVERY → APPLYLIVE
Discovery100%
Match Shown84%
Click-through58%
Apply Started31%
Applied22%
Interview7%
Variant A: BaselineVariant B: +18% lift ★
How I approached it.
- Modeled the discovery → match → click → apply → interview funnel on synthetic data calibrated to public job platform benchmarks.
- Built an agent performance layer that flags underperforming match recommendations for product team review.
- Designed A/B test analysis showing an 18% lift in apply rate for variant B.
- Architected the dashboard so it could plug into real production data with minimal rework.
The outcomes.
- Sent with direct outreach to founders Eric Cheng and Ethan Z.
- Demonstrated I think like both a user and a builder of AI platforms.
- Currently in pipeline.
The stack.
Python
Funnel simulation and statistical testing
SQL
Schema design for production-equivalent reporting
Recharts
Interactive funnel and A/B comparison viz