Scott works as an OSINT analyst collecting, analysing, and disseminating open-source data programmatically for the HAMOC and Honeycomb projects. In this role Scott combines elements of research, social science, data science, software engineering, and dashboard building to produce high quality intelligence for stakeholders. Scott also engages in traditional academic research at Trilateral Research producing literature review’s and conducting empirical research for the Sociotech Insights Group (SIG).
Scott’s background lies in Economics, data analytics, and intelligence analysis – combining economic understanding and principals to human security themes to identify and uncover relationships between real-world variables.
Scott has a passion for uncovering complex real-world relationships through data science processes. Scott developed this interest in his BSc Economics degree at the University of Birmingham and in his MA in Intelligence and International Security at King’s College London.
Scott began using the statistical software Stata in his undergraduate degree which he used for his dissertation to apply a fixed effects linear regression to investigate the relationship between pornographic consumption and sexual violence in England and Wales. In his MA Scott began exploring the R programming language due to its statistical applications, where he applied a random forest classifier to predict whether a domestically radicalised terrorist would be a lone- or group-actor using an open-source dataset. Since joining Trilateral Research, Scott has begun programming in Python given its wide variety of applications – he has written an interactive blog post using Python Dash which attempts to “nowcast” the unemployment rate through a random forest regressor using Google Trends data as features.