I am a freelance chartered statistician specialising in data visualisation and Bayesian modelling. I provide training courses, coaching and consultancy. I've helped analysts from stats and machine learning backgrounds, visualisation/design people, economists, health and social care professionals, academics, civil servants, journalists and more. I'm also interested in helping managers responsible for data science teams (recruiting, managing and quality-assuring) who don't come from that background themselves.
Most teaching statistics and machine learning is not as effective as it could be. I believe that we could do a lot better by helping learners to look at the concepts of models, philosophy of science, inter-personal and communication skills. In particular, I think that data analysis must serve the audience, by finding out where the data came from (quality and complexity) and where the information is going (decision-making). Analyses should then be tailored to fit the requirements, rather than following statistical rituals. My personal philosophical standpoint is here.
I keep active in research and scholarship concerning: meta-analysis, improving statistics education, public service performance indicators, philosophy of statistics, non-parametric Bayesian updating, statistics communication and visualisation, and applications of Lie algebras to high-dimensional Bayesian inference.
I am also an honorary senior fellow at the George Institute for Global Health and a part-time lecturer on Kingston University's MSc course in Data Science.
I started my company, BayesCamp, in 2017. Prior to that, I worked on:
You can get in touch via the links on the bar above, although I especially like real-life letters and postcards: BayesCamp, 16 City Business Centre, Hyde Street, Winchester, SO23 7TA, UK.
I don't use social media.