I work on developing new products at RMT, bringing my specialist statistical modelling skills, as part of a project team
Milan Raicevic
Principal Data Scientist

Career progression

I started my career as an astrophysicist, obtaining a PhD and working as a post-doctoral fellow. I was always very interested in computers and programming, so I made sure to utilize those skills by specialising in computational cosmology. The bulk of my research work was building super-computer simulations to study how light from stars affected the evolution of the early Universe.

After seven years in academia, I moved into the data science field.

The day-to-day work I do today is quite similar to what I was doing as an astrophysicist. The main difference is that instead of generating the data with simulations, I am using real-world data produced and collected by companies I worked for.

My first data science job was for a US health insurance industry company where I used machine learning to efficiently identify likely fraud in insurance claims. Since then and before joining RMT, I applied my skills to problems in agriculture, trading, natural gas, mining and government services.

So far, working in science has taken me from Serbia to the Netherlands, the UK, the US and now Australia. There is so much demand for scientists doing analysis in any field, it can take you anywhere.

What does your role involve day-to-day?

I work on developing new products at RMT, bringing my specialist statistical modelling skills, as part of a project team of around 8 people.

Everyone in the team is hands-on with the development side of things.

We are trying to solve real problems around risk, trying to approach the problem at a scale that has not been tried before. You have to be creative to come up with solutions and you get to learn every day.

The team is really involved and enjoys what we are doing. Every day I get to learn something new which keeps the scientist in me very happy and going.

What skills do you need to be a data scientist?

There is a saying that data scientists are better at statistics then any computer programmer, and are better at programming than any statistician. You need to have a great love and patience for the scientific method. You create a hypothesis and then you test it, rinse-repeat until a new nugget of knowledge emerges and we are able to affect our world in a positive manner.

The current era of big data is completely driven by a rise in available computing power, so cutting edge skills in computer programming are an absolute necessity to work in the field. For me, that’s just another perk of being a data scientist.


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