In a nutshell, I create awesomeness (aka real business value) from data.
I am a data science professional with over 10 years’ experience leading model development and statistical analysis across numerous industries and business domains. My technical expertise is widespread; including machine learning, econometric modelling, time series forecasting, statistics, applied mathematics, operations research and recommendation engines.
I believe a great data scientist excels not only in statistical analytics/machine learning and computer science, but also in commercial acumen and domain expertise – there is most definitely an art to data science.
I am currently the Data Science Manager at REA Group, where I lead the team in driving data-lead decisions and product innovations across the business. Highlights include website and app user behaviour modelling to drive personalised experiences for users, marketing and targeted advertising opportunities for customers; development of recommendation engines to provide personalised suggestions for users; and time-series forecasting to support company budget, planning and target setting.
My studies include a Bachelor of Commerce/Bachelor of Science degree achieved at Monash University, specialising in econometrics and mathematics. I then completed a Master of Economics (Honours). Whilst completing my studies, I assisted with several research projects, and lectured and tutored a number of econometrics and marketing subjects.
I am very much excited about the evolution of data science; the explosion of unstructured data, and advances in technologies to deal with big data. I’m passionate about keeping up to date with the latest innovations and trends, learning, and educating others.
I think Gary King’s (Harvard University) quote best summarises my views on data science – “Big data is not about the data”. With the increasing availability and diversity of data, analytics becomes significantly more important, not less.
YOW! Data 2016 Sydney
Property Recommendations for all Australians
We would like to share our journey and experiences in building a large scale recommendation engine at REA. Attendees will learn about choosing the right algorithms, architecture and toolset for a highly-scalable recommender system.