I'm Jarlath Quinn, the founder of Arcturus Analytics. I started out working in quantitative research before joining the tech industry as a consultant for SPSS UK and spending many years working with customers to help deepen and broaden their understanding of all things analytical.

Since then I've worked in a number of senior technical roles for IBM and SAS Institute designing proof of concepts and delivering consultancy across the entire spectrum of statistics and data science applications.

My background

Some of the application areas I've worked in include:

  • Estimating patient readmission risk for the NHS

  • Preventing asset failure in telecoms and utilities with predictive modelling

  • Developing recommendation engines for employee performance improvement

  • Drug discovery analysis of peptide chains

  • Forecasting product demand across international markets

  • Mining social media for food manufacturers to uncover new insights

  • Predicting student acceptance likelihood for a Russell Group University

  • Modelling claim risk for travel insurers

  • Uncovering product affinity patterns in retail

  • Automating survey analysis for housing associations

Expertise in Data Science

My Expertise

I am an expert in statistical analysis, machine learning and data science applications. Whether it's unlocking a deeper understanding of your data or developing an application to predict outcomes, forecast trends, classify text or estimate risk, I can leverage over three decades of skills and experience to help.

Classical Statistics

We have a deep understanding of classical statistical analysis and the myriad applications that flow from these approaches. Whether it's Survival Analysis, Structural Equation Modelling or just significance testing, we are able to provide trusted guidance and practical help.

Predictive Modeling

Predictive modelling is an art as much as a science. Experience teaches us to lean into the more creative aspects of feature engineering in order to build the best models but at the same time to maintain complete objective detachment when choosing the optimal result.