Consulting and Litigation

I sometimes serve as a consulting statistical expert in litigation matters where data analysis or statistical modeling are central to the legal questions. I conduct and evaluate analyses to assess what the data can support, what assumptions are required, and whether another party’s conclusions are stronger than the evidence allows. I’ve collected some short essays from my experience, in the hope that they help lawyers recognize recurring statistical issues before those issues become decisive.

My work is most relevant when a case depends on samples, models, algorithms, audits, enforcement analyses, or large operational data sets. These issues often arise in disputes involving liability, damages, causation, IP, compliance, fraud, collusion, or classwide proof.

Qualifications

I am Professor and Chair of Statistics and Data Sciences at The University of Texas at Austin, with faculty appointments in Statistics and Data Sciences and the McCombs School of Business. My academic work is about how to draw reliable conclusions from complex data, spanning statistics, machine learning, AI, and interdisciplinary scientific collaborations.

I take pride in explaining complex statistical and machine-learning ideas clearly to non-specialists. I have won multiple university teaching awards, including the Regents’ Outstanding Teaching Award, the highest teaching honor in the University of Texas System. I am also the coauthor of AIQ: How People and Machines Are Smarter Together, a book on the statistical foundations of artificial intelligence written for a broad audience and favorably reviewed by outlets such as the Times and The Wall Street Journal.

Contact

You can reach me at james dot scott at austin dot utexas dot edu. For litigation inquiries, please provide the party names, brief matter description if available, jurisdiction or forum, side represented, relevant deadlines, and a brief description of the statistical or data-related issue. This information is needed for an initial conflict check before any substantive discussion.