Camilo Pestana. Senior Data Scientist at VitalTrace.
What is your academic background?
- Systems Engineering, Catholic University of Colombia, Bogotá, Colombia. 2013
- Honours Computer Science, Edith Cowan University, Perth, Australia. 2017
- PhD in Computer Science, The University of Western Australia, Perth, Australia. 2022
Have you completed any other training in data science?
I’ve completed a FastAI online course and a Machine Learning Course.
What advice would you give to those looking to pivot into a data science role?
Domain expertise is very important in Data Science. You can always use your new data skills to leverage your previous knowledge.
What sparked your interest in working with data?
Many longstanding scientific problems have been solved using machine learning and data. I’m very excited about the future of the field.
How did you come to work in your current role with VitalTrace?
I was previously a lecturer at UWA. When the company was looking for a Data Scientist, one of my graduate students recommended me for the role.
What sort of projects have you been working on?
Computer Vision for manufacturing. Also, providing insights from animal trials and creating tools to leverage and speed the work from other teams in the company.
What tools/platforms do you use in your work?
- Libraries: Matplotlib, Pytorch, Seaborn, Jupyter Notebooks
- Cloud: AWS
What has been a highlight of your data science career so far?
I had the opportunity to work on project financed by DARPA last year. Also, two years ago I proposed a project to the Colombian government to use computer vision to detect abnormalities in CT scans to “detect” COVID-19. Working with some of the best academics in Colombia, the project attracted the equivalent funding of $800k AUD. Presently the system is implemented in 5 hospitals in Colombia.
What challenges have you faced as a data scientist?
Real-life data is messy! If you believe everything is like a Kaggle competition where all datasets are cleaned… Think twice! Real-life projects might be very challenging but also very rewarding. Thinking outside the box is a must.
What are some of the big areas of opportunity you want to tackle in this space?
There are many opportunities to work in the area of ethical AI, robustness, explainability and fairness.
What excites you most about recent developments in Data Science?
What does the future of data science look like?
It will be more specialised. There will be hardly anyone with the role “Data Scientist”. But there will be many more areas of knowledge that will surge from it. We are now the pioneers that will shape the future of Data Science.
For people considering a career in data science, what is one piece of advice you would give?
Now is the best time to start.