Dr Andrew Jones

Senior data scientist

Located in Brisbane

Bachelor of Engineering (Mechatronic)
(University of Queensland)

Bachelor of Arts (Mathematics, Hons I)
(University of Queensland)

Doctor of Philosophy (Applied Statistics)
(University of Queensland)

About Andrew

Andrew Jones is a highly skilled data scientist, specialising in applied statistical analysis. He has extensive experience in coding and model development using Microsoft Excel, VBA, R, Python and C++.

He also has an outstanding applied knowledge of the NSW Project Impact Assessment with Measurement and Verification (PIAM&V) method.

Andrew has successfully project managed software development for statistical analysis and machine learning projects, frequently dealing with big data. He has worked in mathematical statistics research, co-authored a number of academic publications and been part of several successful grants.

Career highlights

  • Published leading research into genetic estimates of effective population size to fisheries management. This research has also been presented at several conferences.

  • Developed and used tools for the measurement and verification of energy savings under the NSW Project Impact Assessment with Measurement and Verification (PIAM&V) method.

  • Developed a methodology to robustly estimate the volume of white matter lesions in MRI images for a sub-study of the massive ASPREE (ASPirin in Reducing Events in the Elderly) study.

  • Written packages for the R programming language which he has made available on the Comprehensive R Archive Network (CRAN).

Andrew has co-authored the following academic publications:

  • Jones, A. T., Ovenden, J. R. and Wang, Y. G. “Improved confidence intervals for the linkage disequilibrium method for estimating effective population size.” Heredity 117.4 (2016): 217.

  • Nguyen, Hien D., Andrew T. Jones, and Geoffrey J. McLachlan. “Stream-suitable optimization algorithms for some soft-margin support vector machine variants.” Japanese Journal of Statistics and Data Science (2018): 1-28.

  • Nguyen, H. D & Jones, A. T. “Big Data-Appropriate Clustering.” chapter to appear in Data Analytics: Concepts, Techniques and Applications. CRC Press, Taylor & Francis Group, USA.

Issue and service expertise

Andrew has performed statistical analysis and experimental design for projects spanning a wide range of topics including image analysis, clinical studies, natural resource management, energy usage, the energy efficiency industry, behaviour change, supply chain networks and text mining.

Previous roles

In addition to his ongoing academic research and consulting roles, Andrew previously held roles in engineering, university mathematics tutoring and completed a Phd in applied statistics.