What is your academic background? 

I did my Bachelor’s in Life Sciences (Environmental Biology) at the National University of Singapore, and then went on to complete a Master’s in Environmental Science at the University of Western Australia.

Have you completed any other training in data science? 

Definitely. There are so many online courses and webinars out there now that anyone can complete in their own time, and there is no shortage of new skills to learn. Things change so quickly in this field and there is often more than one way to solve problems, so it is important to constantly upskill and learn new ways to do things.

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?

During my studies I completed a work experience program at an environmental consultancy focused on contaminated land and groundwater, which involved very basic GIS and remote sensing. From there I realised I wanted to do a lot more of that, working with geospatial and Earth observation data, and so that was what I looked for in a full-time job after graduating. My advice for others would be to explore their interests and how they can be applied to data science, grab opportunities to volunteer or gain work experience, and be willing to learn and upskill.

What sparked your interest in working with data?

My interest was piqued while learning to use R for statistics during my undergraduate studies. When I dived into GIS and remote sensing in my graduate studies, I once again saw the importance of being able to manage, analyse and visualise data. I was particularly interested in how the same set of data can be presented in vastly different ways.

How did you come to work in your current role?

I got to know a friend of a friend working in this space. She put me in touch with someone working at NGIS, and I loved the prospect of being able to work on a variety of interesting and meaningful projects using geospatial data.

What sort of projects have you been working on?

The largest project I have been working on is the Group on Earth Observations – Google Earth Engine (GEO-GEE) Program, where we are supporting 32 projects across the world in using Earth observation data to tackle global challenges including climate change, disaster management and poverty alleviation.

What tools/platforms do you use in your work?

I use Google Earth Engine, Google Cloud Platform, Colab notebooks, ArcGIS, QGIS and FME. I normally code in Python or JavaScript.

What has been a highlight of your data science career so far?

I love that the job has given me opportunities to learn new skills in data science, but a highlight has probably been being able to pass on those skills by running training courses for others interested in exploring the field or adding on to their existing skillset.

What challenges have you faced as a data scientist?

There is always the challenge of data availability and data quality. Working as a consultant there is also the challenge of understanding what your client needs and how to turn their vision into reality, as well as communicating the science to them.

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

I’m interested in how Earth observation data can be integrated with environmental data and creating robust models to accurately predict environmental changes, especially in (near) real-time.

What excites you most about recent developments in Data Science?

The increased processing power that comes with cloud-based platforms eliminating the need to download terabytes of data and run analyses on a local machine is pretty exciting, as well as the increased availability of (near) real-time, high resolution, high quality Earth observation data.

What does the future of data science look like?

With the rise in availability of data and analysis tools, data science is going to be more and more accessible for anyone to dip their toes into. However that is where there will be a need to discern the quality of data and ensure that results are validated.

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

Ask questions, be curious, be proactive. Never stop learning.