How we measure and rank expert talent
Transparent methodology, public leaderboard logic, and evidence-backed case studies.
Methodology version: v1
Methodology
How we score experts, rank candidates, and publish leaderboard data
Expert Index Score (EIS)
A composite score from interview performance, micro-task outcomes, peer-review quality, and historical task delivery signals.
MetaMatch Semantic Ranking
Task and expert embeddings are reranked with quality, availability, and cost-fit constraints for final shortlist ordering.
Offline + Online Validation
Ranking and scoring policies are validated against holdout outcomes and monitored through production scorecards.
Leaderboard Construction
Leaderboard entries are anonymised and refreshed on a recurring snapshot pipeline with methodology versioning.
Case Studies (Anonymised)
Directional outcomes from selected engagements on the platform
Case study benchmarks are updated as new engagement reports are published.
LLM RLHF Data Quality
+34% acceptance_rate
Expert-generated preference pairs improved model-eval acceptance versus crowd baselines.
Disclaimers & Limitations
Please read before using MetaAnalysis scores in hiring decisions
- 1.EIS is an AI-generated estimate, not a credential or professional certification.
- 2.Leaderboard rankings reflect activity within the MetaAnalysis platform only.
- 3.MetaMatch scores are probabilistic and should be used as one signal among many.
- 4.Benchmark datasets are curated and may not represent all real-world distributions.
- 5.Case studies are directional outcomes and not universal guarantees.
Data Privacy in Research
Research outputs are based on anonymised, aggregated data and never disclose personally identifiable information.
Explore the leaderboard
See how top experts rank by domain across the MetaAnalysis network.
View Leaderboard