What is your academic background?
I originally completed a bachelor’s degree in engineering, followed by a postgraduate diploma specialising in petroleum engineering from Instituto Tecnologico de Buenos Aires (ITBA) university in Argentina. I later moved to Australia to pursue a PhD in engineering at Curtin University, which I completed in 2018.
Have you completed any other training in data science?
I attended the Software Carpentry workshops at Curtin University, which aim to teach programming skills to researchers – first as a learner and later as a workshop helper. I also took a few statistics refresher classes throughout my PhD. Then prior to my Data Science internship at Woodside, I completed the MITx course “Introduction to computer science and programming in Python”.
If you pivoted into a data science role from another area, how did you go about this and what advice would you give others looking to do the same?
I would advise people to identify their transferrable skills that are relevant to data science roles and lean on them when applying to jobs. For example, I worked as a sessional lecturer while at Curtin. The communication skills I picked up come in handy whenever I have to explain complex concepts to people in the business. The ability to break down problems into manageable chunks is also really important, which I learned through my engineering and research experience.
What sparked your interest in working with data?
My PhD research was experimental in nature, which meant that I had to analyse lots of laboratory data. The files ended up being so big that MS Excel kept crashing, so I turned to the next thing I could find in the IT catalogue: MATLAB. I learned to write code through trial and error (and lots of Googling!) and fell in love with the automation possibilities. As I was nearing the end of my degree, I realised that you could get paid to solve business problems using programming skills and decided to pursue a data science career.
How did you come to work in your current role?
I did a Data Science internship during the last summer of my PhD, after which I was offered a full-time role upon completion of my degree.
What sort of projects have you been working on?
I have worked on a wide range of projects in the energy space. Some of them are relatively simple and straightforward, involving a lot of exploratory analysis on oil & gas process data to produce reports and generate insights. Some of them are more complex (and fun!) involving the use of machine learning models on geoscience data to predict reservoir rock properties. More recently, I have worked on modelling renewable power sources using open-source climate data.
What tools/platforms do you use in your work?
Most of our work is done in R and Python. However, the ability to pick up new tools and programming languages is probably more important as some projects require us to work with specialised software used by the business.
What has been a highlight of your data science career so far?
A location-allocation model that we built along with the geospatial team last year. This required me to not just learn about GIS concepts and specialised tools, but also to find ways to communicate cross-disciplinary concepts and results back to the customer.
What challenges have you faced as a data scientist?
Understanding the real business problem to be solved, which often doesn’t match what the customer first asks for. After projects are correctly scoped and funding is secured, getting access to the right data is probably the second biggest one. Even when data is available, sometimes the quality is not good enough to derive any meaningful conclusions or there are limitations. Outcomes sometimes do not match initial expectations, so fluent communication with the customer is key to preventing surprises.
What are some of the big areas of opportunity/questions you want to tackle in this space?
The integration of data-driven and physics models is something that I would like to explore further. Ideally in the low-carbon and green energy space.
What excites you most about recent developments in Data Science?
I’m really excited to see how data science can help to transform the energy space, especially around cost optimisation of renewable and low-carbon power options.
What does the future of data science look like?
I think we will start seeing more citizen data scientists embedded in the business, who will have the right skills to solve data problems within their knowledge domains and take on continuous improvement initiatives. There will still be a place for centralised data science teams in non-tech companies, but they would be focusing on more complex work scopes and acting as liaison with the academic and research communities.
For people considering a career in data science, what is one piece of advice you would give?
Stay curious and open-minded, there is always something to learn. Technical skills are important, but it is the combination with business and communication skills that will take you further in your career. Lastly, don’t underestimate the power of networking as every problem you encounter in industry will likely have been solved before, it’s only a matter of finding the right people and learning from them.