Statistical Evidence & Quantitative Analysis
Statistical methodology, regression, inference, forecasting, uncertainty, anomaly analysis, reproducibility, and evaluation of whether quantitative evidence supports the conclusion being asserted.
Statistical Evidence · Model Validation · Algorithmic Decision Systems
Independent statistical analysis for consequential decisions and disputes.
Stuart Jones Consulting evaluates statistical evidence, predictive models, data quality, machine-learning systems, and algorithmic decision processes for litigation and other high-stakes technical matters.
Technical questions become harder when they leave the laboratory.
A model can be statistically sophisticated and still be inappropriate for the question being asked. A dataset can contain millions of observations and still fail to support the conclusion drawn from it. A machine-learning system can perform well on a benchmark and behave very differently when introduced into an institution, market, or decision process.
My work focuses on that boundary: the point where statistics, data, models, and algorithms begin affecting real decisions. I help clients examine whether the underlying data are fit for purpose, whether analytical methods are appropriate, whether models have been adequately validated, whether results can be reproduced, and whether the conclusions actually follow from the evidence.
Quantitative work where the details matter.
Predictive Model Evaluation & Validation
Evaluation of model specification, performance, stability, validation, interpretability, subgroup behavior, and the quantitative evidence supporting model-performance claims.
Litigation & Expert Consulting
Independent technical analysis involving statistical evidence, data science, predictive modeling, machine learning, AI, data quality, and algorithmic decision systems.
Quantitative support for litigation.
Data-intensive matters often turn on whether an analysis can actually support the conclusion being asserted.
- Can an opposing analysis be reproduced?
- Are the assumptions doing the real work?
- Does the statistical method fit the question and the data?
- Has a predictive model actually been validated?
- Does an algorithm measure what is being claimed?
- Do data limitations change the conclusion?
- Would reasonable alternative choices change the result?
- Can the technical record be explained clearly?
Consulting Expert
Confidential quantitative and technical support, including case assessment, analytical review, reproduction of results, and identification of methodological issues.
Testifying Expert
Independent expert analysis and opinions within appropriate areas of expertise, including reports, deposition, and testimony when warranted by the engagement.
Statistics first. Technology in context.
I am a statistician whose applied work spans data science, predictive modeling, model evaluation, and quantitative systems used in consequential settings.
I currently serve as a Chief Data Scientist in insurance regulation, including structured technical review of insurer-submitted AI and machine-learning models used in pricing, ratemaking, and underwriting, along with statistical analysis, data quality, and methodological review.
I hold an M.S. in Statistics from Auburn University and previously taught mathematics and statistics at the college level. I also serve as a data science and artificial-intelligence instructor.
Across those roles, a recurring theme has been the difference between producing an analytical result and determining whether that result is reliable enough to inform an important decision. That distinction sits at the center of Stuart Jones Consulting.
Methods, models, data, and decisions.
Statistical evidence
Regression, inference, forecasting, uncertainty, anomaly analysis, and whether quantitative conclusions follow from the data.
Predictive model evaluation
Specification, performance, stability, validation, interpretability, subgroup behavior, and limitations.
Data quality & reproducibility
Integrity, transformations, lineage, analytical pipelines, and reproducible workflows.
Machine-learning model review
Technical evaluation of model performance, stability, interpretation, variable relationships, and supporting evidence.
Algorithmic decision systems
Quantitative systems used to support consequential institutional and organizational decisions.
Applied insurance analytics
Technical model review and quantitative analysis in insurance and regulated-data environments.
Independence is part of the work.
Conclusions are not contingent on the outcome of an engagement, and compensation is never tied to the substance of an opinion or the result of a matter. Potential engagements are subject to conflict review and an assessment of whether the requested work falls within the appropriate scope of expertise.
Discuss a matter.
Provide a brief, non-confidential description of the technical issues involved. Conflict review comes before the exchange of case materials.