(This article was originally published at Hyndsight, and syndicated at StatsBlogs.)
We have an exciting new initiative at Monash University with some new positions in business analytics. This is part of a plan to strengthen our research and teaching in the data science/computational statistics area. We are hoping to make multiple appointments, at junior and senior levels. These are five-year appointments, but we hope that the positions will continue after that if we can secure suitable funding.
What is business analytics?
You can think of “business analytics” as the application of data analysis to business problems. For example, analysing point-of-sale data, or customer churn, or fraud identification, or credit risk analysis. We might have called it “computational statistics”, “data science”, or “machine learning”, or “operations research”, or even “big data”. But we are part of the Monash faculty of business and economics, and our Dean was keen on the name “Business Analytics”, and since he pays the bills, his view prevailed. I’m not fussed what it gets calls, I’m more interested in good analysis applied to interesting problems, regardless of the name used to describe the activity.
When I mentioned these jobs to Terry Speed last week, he made a painful noise and said “I guess I might get used to the name”. He would have preferred the word “statistics” in there somewhere. When I attended an Operations Research conference last year, I heard similar complaints from OR people about their discipline getting left out in discussions about data science problems in business. My view is that anyone doing data analysis involving business problems should be aware of the useful perspectives provided by statistics and operations research, and be familiar with the many tools and models that have arisen in those communities. But they should also be aware of perspectives and tools that have arisen out of the machine learning community. I have long thought that the name “data science” usefully covers everything to do with data analysis and we would be better off if the various groups joined forces under that title. But that was too general a term for these jobs as we do have a specific business focus, and hence we ended up with “Business Analytics”.
Who should apply?
The people we are seeking will be able to teach subjects on data visualization and statistical learning, and do research in related areas. They may have a background in machine learning, statistics, econometrics, actuarial science, operations research, or any other similar discipline involving the analysis of lots of data.
Some real experience with data analysis problems arising in business would be very helpful. That might mean having worked full-time as a data scientist, or having worked as a statistical consultant, or having a high ranking as a kaggle competitor.
Everyone appointed to an academic position at Monash University is expected to have an excellent research record, or have demonstrated the potential to be an excellent researcher. So we will be looking for people whose CVs list good papers in top journals.
Finally, the successful candidates will teach subjects on business analytics. So the ability to be an inspiring lecturer and clear communicator is important.
We would like to appoint people at junior and senior levels. Anyone appointed at the level of professor or associate professor will be expected to already have a strong international profile in the area, and be able to provide leadership in business analytics. Anyone appointed at the level of lecturer will probably have only recently completed a PhD (or will be completing in the near future), and be just starting an academic career.
What happens after five years?
We currently have funding for these positions for five years. Obviously we don’t want to start a new initiative, generate lots of activity and interest, and then have everyone leave because we don’t have further funding. So one of the first tasks of the more senior appointments will be to consider how to continue the business analytics initiative beyond the first five years.
Why come to Monash?
I joined the Department of Econometrics and Business Statistics in 1998 and I’ve enjoyed working here so much that I’ve never left. After more than 16 years, I can’t imagine finding a better place to work. It is a wonderfully friendly, supportive, stimulating and encouraging environment.
It is also a place with a lot of fantastic people to interact with. We are unique in having strong research in econometrics, statistics and actuarial science. The combination of perspectives makes it very interesting and I’ve learned a lot from being part of a group containing many people with different training and background from me.
Although rankings are always a little dodgy, they provide a rough guide to relative strengths. According to IDEAS rankings (which covers economics), we are ranked:
in the top 10 departments in the world in econometrics
in the top 10 departments in the world in time series
in the top 15 departments in the world in forecasting
The QS world rankings of universities place Monash 24th in economics and econometrics, and in the top 100 in statistics and operational research.
How do I apply?
The official advertisement provides details on how to apply. In your application, it would be helpful if you indicated what level (B, C, D or E) you thought you were most suitable for. If you have specific questions about the job, you can email me. But please don’t send me your CV asking for my comments. We expect a lot of applications and I cannot provide personal comments on individuals. This post should answer most questions about who is suitable, and what we are looking for. If you think you fit the description here, then please apply.
Please comment on the article here: Hyndsight
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