Data Analyst Says to Go for the Whacky Ideas & Jobs

Name: Henry Zovaro (he/him)

PhD: Astronomy & Astrophysics, The Australian National University, 2020

What is your current job?

I am a Data Analyst for the National Emergency Management Agency in Canberra, ACT, Australia.

I conduct data analysis in topics relating to emergency management - for instance, looking at insurance coverage, investigating demographic profiles of areas affected by disaster, researching charities that work in the emergency management sector. The depth of analysis varies - some analysis tasks are very straightforward (e.g. what is the average income in a suburb affected by a recent cyclone?) whereas others are much more involved (e.g. what socioeconomic factors affect insurance purchasing behaviour?). I do all of my analysis in Python.

A large part of my job is tracking down the appropriate datasets to fulfill a certain task, and making maps using GIS software for data visualisation. I often have to write up my results in the form of technical reports and/or present my findings to stakeholders at meetings. I also mentor junior staff and do occasional administrative work as needed.

What is your favorite thing about your job?

I enjoy the feeling of support I get from my team, which I didn't have as a postdoctoral researcher. If I need to take time off for any reason, it's an expectation that my coworkers will take over my responsibilities until I'm back at work. In a similar vein, if I'm struggling with the workload, I can ask my team for help, and I know they'll be there to support me.

It's made me realise how isolated and overwhelmed I felt as a postdoc - I could never take time off without feeling guilty, because I never felt that I could ask anyone to carry on my work while I was gone.

What is the most important skill you developed or experience you had during your PhD that now helps you in your current position?

Definitely my critical thinking skills. As a PhD researcher, most of the data I dealt with was noisy and imperfect. This has paid off in my current role, where I often deal with datasets that are biased or have lots of entries missing - it has given me an intuitive understanding of how much to trust anything you derive from a particular dataset, and turn this into an actionable recommendation.

How did you build the skills necessary for your current role?

I found that my field of research prepared me very well for my job as a data analyst without needing to go out of my way to learn any additional skills. That said, I developed an interest in software engineering during my research career, and so I ended up learning about modern development practices and incorporating them into my work. Whilst not the main focus of my current role, it has certainly helped make my coding much more efficient.

How did you find this position? What were the career steps you took to get to where you are now?

The central Australian government website for jobs in the Australian Public Service.

PhD graduate ➡️ postdoctoral fellow (a few short-term contracts) ➡️ Data Analyst

If someone is interested in a similar role, what would you recommend they start doing now to prepare?

Get used to working with different data types; learn how to code for data analysis in python/R; and most importantly, learn your statistics!! Understanding stats is crucial to being a data analyst, especially when it comes to interpreting your findings and translating them into trustworthy recommendations.

Why did you decide to not pursue a career in academia?

It ended up being my only option, after a series of poor strategic choices during my postdoctoral career that left me uncompetitive for the next postdoc - namely, saying yes to too many projects where I provided support, rather than prioritising my own research projects.

The other factor in my decision was having a first-hand view of what it was actually like to be a professor - long hours, very limited time for research, teaching responsibilities, admin work - which was a far cry from the idealistic picture I'd imagined when I first started out in my field. Besides, I was at the point where I really needed stability and certainty for the sake of my own mental health, and I was definitely not going to find those in an academic position!

What advice do you have for someone getting their PhD and looking to pursue a career outside of academia?

1. Enjoy being a PhD student while you can. Being able to dictate the course of your own research is such a rare opportunity to be given. My advice is to follow those rabbit holes, be curious, spend time trying out whacky ideas, because you don't get the luxury to do so in most jobs.

   
   

2. Also, if you feel that you have to leave academia not out of choice but out of necessity, try not to be too bitter about it - it can really taint your memories of what should be a wonderful life experience.

   
   

3. And finally, have fun with it - embrace the unknown! Once you leave academia, your options will open up like never before. Apply for that whacky-sounding job - you never know what it might lead to.

Are there any components of your identity you would like to share, including how they have impacted your journey?

I am transgender and transitioned during my PhD. I was extremely fortunate to work at an institute full of open-minded and accepting individuals, and as a result I can't say that it had any negative impact on my career at all. In fact, I was able to concentrate and work much more effectively after I came out.

And for those interested, what was your main area of research?

Every galaxy is thought to contain a supermassive black hole at its centre, and sometimes these black holes generate fast-moving jets of plasma that glow brightly at radio wavelengths. My PhD research focused on the role that these jets play on the evolution of their host galaxy, in particular how they affect star formation processes.

To do this, I collected and analysed data from some of the world's largest telescopes. My research involved writing code for data analysis, e.g. model fitting or to implement scientific algorithms for computing physical quantities, and data visualisation.