Jon Minton is a senior statistician and applied data scientist with 11 years of experience blending academic rigour and public-sector impact across top UK institutions. He specialises in risk modelling, epidemiology and Bayesian methods, with a strong publication record and ~1,500 citations informing policy on mortality, fertility and housing economics. At Public Health Scotland he translated complex epidemiological findings into actionable national guidance during COVID-19 and led innovations in mental health monitoring and agile practice within public health teams. Technically fluent in R, Python, SQL and software development (SCQF Level 8), he builds interactive visual analytics with Shiny and Plotly that reveal subtle socioeconomic disparities. Now at Smith+Nephew, he positions quantitative insight for risk consultancy, data engineering and advanced analytics, combining policy-facing communication with reproducible code and visual storytelling. An uncommon strength is his ability to move methods from peer-reviewed research into operational tools that shape real-world health and housing decisions.
11 years of coding experience
2 years of employment as a software developer
Master of Arts - MA, Critical Theory & Cultural Studies, Pass, Master of Arts - MA, Critical Theory & Cultural Studies, Pass at University of Nottingham
Doctor of Philosophy - PhD, Quantitative Sociology and Human Geography, Pass, Doctor of Philosophy - PhD, Quantitative Sociology and Human Geography, Pass at University of York
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