Alex Rohl

 

Alex Rohl. Data Scientist at Defence Science Technology Group.

 

What is your academic background? 

I did a double major in maths and data science through the Bachelor of Philosophy program at UWA.

 

Have you completed any other training in data science? (Up-skilling, MOOCS, short courses etc)

No, but I completed many hackathons throughout my university years. What’s great about data science is that you learn while doing.

 

If you pivoted into a data science role from another area, how did you go about this?

My “pivot” to data science is better described as a merging of coding, maths and applied problem-solving – which I have always enjoyed.

 

What sparked your interest in working with data?

Working with data is essential to solving real world problems!

 

How did you come to work in your current role with Defence Science Technology Group?

Having completed internships in various sectors – energy, health, mining and even astrophysics, I looked to defence to apply my skills to a new industry and applied through their Graduate Program.

 

What tools/platforms do you use in your work?

Every day I am using Jupyter Notebooks.

 

What challenges have you faced as a data scientist?

With every project, by far the toughest challenge is getting up to speed with the domain knowledge essential to understand the problem. However, by asking the experts lots of questions (who are always happy to brag on about their vast knowledge!), one can learn on the job effectively.

 

What are some of the big areas of opportunity/questions you want to tackle in this space?  

Currently, I am extremely interested in machine learning applications to the cyber security domain – as this is a domain rich with data and effects so many industries.

 

What excites you most about recent developments in Data Science?

Recent developments in Natural Language Processing (NLP), have revolutionised the way industries understand and model language data – particularly within social networking sites.

 

What does the future of data science look like?

In the future, I think data science will continue to become polarised. On the one hand, data science tools will become more and more accessible to small businesses, and anyone will be able to implement models for themselves. On the other, data science research is growing at such a rapid pace, there will be a huge number of researchers pushing the boundaries of the field.

 

For people considering a career in data science, what is one piece of advice you would give?

Without a doubt, work experience is the best way to learn data science. By truly understanding a client’s problem, their software infrastructure and nuances in their datasets, and then to build an innovative data-driven solution; is how a career in data science is made.