Consultants Directory

We provide a Directory of Statistical Consultants listing our professionally qualified members who offer a statistical consultancy service. Professionally qualified members hold the status of Chartered Statistician (CStat).

The Directory contains profiles created by the consultants, including information on their specialisms and background as well as their contact details. It operates on an opt-in basis – each consultant has agreed to their profile being available on our public website. There are terms of reference covering the operation of the directory, to ensure it remains up to date.


Andrew Butler FMG Consumer Healthcare OralCare


Ranjith Perera Scorecards; Credit, Market,Operational and Conduct risk (model building,monitoring and validation); IRB and IFRS models; Stress testing; Forecasting and impairment modelling; General statistical modelling and SAS and R programming Europe


Camille Szmaragd Veterinary epidemiology, statistical modelling, computational statistics, multilevel modelling and MCMC simulations.


Alison Colyer I have experience analysing a variety of data types such as qPCR, sequencing, behavioural or dental measures etc., which incorporate statistical complexities such as repeated measures, non-linearity, multivariate, censoring and multiplicity.
Gilbert Mackenzie Statistical Model Development. Survival and Event History. Non-PH and & Parametric Models. Covariance Modelling in longitudinal studies.Design and Analysis of Longitudinal RCTs. Interval Censoring. GLMMs. R. SPSS. I was a Medical statistician for 30 years in QUB , Belfast, UK; Professor of Medical Statistics at Keele University in the UK and Director of the Centre of Medical Statistics; Professor of Statistics in the University of Limerick and Director of the Centre of Biostatistics and finally Visiting Professor of Statistics at ENSAI, Bruz, Rennes, France.


Kerry Gordon Drug development, clinical trials


Shlomo Sawilowsky 1. Standardized Test Development. 2. Expert testimony: quantitative and qualitative research design, psychometrics, quantitative and qualitative data anlaysis; quantitative and qualitative program evaluation. 3. External Evaluator for funded research in education, psychology, engineering, etc.


Dr. Abdel-Salam Abdel-Salam Economic Capital Models, Regression Analysis, Mixed Models, Nonparametric and Semiparametric Regression, Exploratory and Robust Data Analysis, Profile Monitoring, Biostatistics, Quality Control, Quality Improvement, Industrial and Medical Applications, Operations Research, Multivariate Analysis, Health-related monitoring and prospective public health surveillance.


Jonathan Cook Design and analysis of clinical trials including sample size calculations. Expertise in surgical research. Presenting statistical evidence to lay audiences and to groups with varying levels of expertise in the subject matter.


Ian Hunt


Oyelola Adegboye Spatial and spatio-temporal models, environmental statistics, disease mapping and mixed models data


Matt Homer Rasch modelling Multi-level modelling Latent class analysis Causality


Anne Pinot De Moira I have a very strong background in statistics. I have first-hand experience of both qualitative and quantitative research. I have analysed data using parametric and non-parametric statistics, using sophisticated models but also using graphical representation to simplify findings. I regularly deal with data extracted from large datasets, collected by experimental design and resulting from questionnaires. In the context of my assessment and education research, I have considered a diverse range of issues relating to standards, the design of mark schemes, the identification of errant examiners, learner approaches and marking reliability. I maintain a keen interest in new developments in the field of statistics but regard the communication of statistical analyses as my greatest strength.
Piotr Fryzlewicz Statistics and data science consulting services in: statistical modelling and simulation; time series analysis including time series forecasting; predictive analytics including predictive regression modelling; statistical computing in R; statistical learning and machine learning including deep learning; change-point detection; high-dimensional statistical inference and dimension reduction; quantitative finance. Statistics and data science training courses in: general statistics and machine learning, predictive regression modelling, deep learning with R and Keras, time series analysis including forecasting, basic and advanced R, statistics in finance. (Please contact me if you are interested in a course on a topic not listed here.) Worldwide


Shaun Flanagan Data Collection; Data Quality; Data Governance; Data Analysis, Data Systems, Scoping Data & Management Information needs, Team leader; GDPR, Operating at senior levels including Ministers and CEO's Embedding a data culture across an organisation


Gemma Hodgson Statistics Training, Analysis & Consulting in the sensory & consumer, manufacturing and pharmaceutical industries. Some typical examples could be: Understanding how to design experiments to make them efficient and increase chances of finding an answer to your problem; A hands on course using a stats software package in fundamental statistical concepts (e.g. variance, confidence intervals, p-values, hypothesis testing, analysis of variance (ANOVA), correlation and regression) to help you get started; Principal Components Analysis (PCA) to understand how variables are linked; Partial Least Squares (PLS) Regression to link sensory panel data and consumer liking or intent to purchase data...and many more! As a small business we do not have the red tape and constricting procedures that some big companies bring so we are able to be completely flexible - no problem too big or small.


Hon Yin Hau Scorecard, Information Value, Weight of Evidence, Linear / Logistics / Non-linear / Quantile regression, Automation, Time Series, Simulation, GLM, Survival Analysis, Risk Modelling, Pricing, Forecast, Data Cleansing, Experimental Design, Financial Modelling, Graphical Design, Erlang-C Model, Tree Decision Program: SAS, R, VBA, SQL, Tableau Worldwide


John Mckellar Pharmaceutical Statistics Key areas commonly supported: Pharmaceutical; Medicine, Medical; Devices; Submission; Insurance; R&D; Regulatory; Training; Logistics; Sales; Marketing; Manufacturing. Key words for statistical techniques: Sample size; Design of Experiments; Surveys; Questionnaires; Data Management; Data Quality; Exploratory data analyses; Data summaries; Classification models. Worldwide


Anthony O'Hagan Bayesian methods and applications, particularly developing novel solutions to difficult problems. Elicitation of expert knowledge. Assurance analysis for drug development. Cost-effectiveness analysis for pharmaceuticals. Training in Bayesian statistics, cost-effectiveness analysis, elicitation, model uncertainty. Some experience in legal work. Worldwide


D.M. Basavarajaiah Statistical theory and Methods, Statistical modeling of data from broader field of life science, including Social research, Operational research, Human genetics, Genetic Statistics, Biomedical and clinical research.