Team
Rankquant's editorial team combines quantitative, data, and statistics backgrounds. Our methodology is applied mathematics, not proprietary secret sauce — and our editorial independence is why the whole procedure is written down at /methodology: which reviewers qualify, the shrinkage constant K = 53 behind the AI-adjusted percentile, the 1.645 multiplier behind the published confidence interval, how each cohort is defined, and how a reviewer-normalized mean becomes a percentile.
Ryan Siegal
Founder and Principal
Quant-finance founder (multi-strategy hedge fund, event-driven trader at Alpine Global) turned consumer-review publisher. Applies the statistical rigor of trading systems to product reviews. Washington University in St. Louis alum, based in New York.
Expertise: Statistics · Quantitative Investing · Event-Driven Strategies · Merger Arbitrage
Ben
profile pendingConsulting Statistician
STEM-background statistician advising Rankquant on distribution analysis, Bayesian priors, and shrinkage-constant calibration.
Expertise: Statistics · Bayesian Inference · Meta-analysis · Distribution Analysis
Josh
profile pendingConsulting Data Scientist
Applied data scientist advising on empirical review-data analysis, ingestion-pipeline design, and outlier detection.
Expertise: Data Science · Data Engineering · Outlier Detection · Machine Learning
Yang
profile pendingConsulting Quantitative Analyst
Quantitative analyst advising on normalization calibration and the cross-category applicability of the framework.
Expertise: Quantitative Analysis · Statistical Modeling · Category Theory · Calibration