ECSSN SSA and NZSA Joint Webinar Series: Academia vs Industry: A statistical viewpoint
Time: 2 pm AEDT and 4:00 pm NZDT.
Statistics is a skill that is essential for progress in multiple environments. In academia, we apply these skills to understanding measurements produced in research. In industry, these skills are required to make sound decisions based on the best available information. However, the mindset needed to approach problem-solving in these sectors has many subtle and sometimes not-so-subtle differences.
In this talk, she will discuss, based on her personal experience, aspects of academia and industry that differ and the pros and cons for each. This will include the level of statistical difficulty, biased perceptions between workers in each sector, timeframes, technology, extraneous responsibilities, communication, teams and administration.
Dr Clair Alston-Knox, Senior Statistician, Head of Visualisation Development and Statistical Modelling, Predictive Analytics Group
Clair began her career as an applied statistician with NSW Agriculture as a biometrician at a remote location. After completing a Master of Science (Research) in the area of longitudinal analysis of Poisson data and Generalised linear models, she left this position to study for a PhD in using Bayesian Mixture Models to analyse sheep CT images. The research question driving this thesis aimed to maximise the information available to livestock managers to make decisions for drought management.
After completing her PhD, she worked as an academic in applied statistics, specifically, Bayesian analysis, for several years at QUT and Griffith University. At Griffith, exposure to a wide range of social and behavioural science research lead to her desire to take up her current industry role, which combines industry consulting, research and software development. A recent highlight of this role has been working with a team that obtained a Business Research and Innovation Initiative (BRII) grant to examine the feasibility of combining microwave technology and machine learning to predict asbestos presence in building materials. She is also currently collaborating with a research team at Murdoch University in the area of accreditation for new technology in predicting carcase traits. In this role, she sees a wide range of projects, including government and academic consulting, as well as maintaining research links via student supervision and early career researcher mentoring.