Improving Signal-to-Noise Ratio in Exchange Hiring Data: Our Method
We process thousands of job postings weekly across 10 exchanges. Most of it is noise. Here's how we find the signal.
The Noise Problem
Large exchanges (Binance, Coinbase) post 50-100 new roles weekly. Most are routine: backfill, seasonal hiring, standard operations. If we treated all of them equally, the signal would be buried.
Our Five Noise-Reduction Techniques
1. Baseline Normalization
Every role is measured against the exchange's rolling 12-month hiring average for that category. A role is only "signal" if it's above baseline — not just present.
2. Role Specificity Weighting
Generic roles ("Software Engineer") get low weight. Specific roles ("Perpetual Futures Matching Engine Engineer") get high weight. We've built a role specificity scorer that processes each JD title and description.
3. Temporal Clustering
Isolated roles are weak signals. Clusters of related roles posted within the same 30-day window are strong signals. Our clustering algorithm identifies role groups by semantic similarity.
4. Multi-Source Confirmation
A role that appears on LinkedIn only might be exploratory or outdated. A role that appears on LinkedIn AND the company career page AND an ATS is almost certainly a real, committed hire.
5. Analyst Review Gate
High-scoring signals get human analyst review before becoming predictions. The model finds them; humans decide if they pass the final quality gate.