A little about me

I thrive on solving complex real world and scientific problems – It’s what I’ve been doing my entire career and I can’t imagine stopping anytime soon. I’m naturally drawn to creatives, grassroots movements, and ideas that challenge the status quo. I have a penchant for creating high-value, high-impact products with limited resources and I take inspiration from everything and everyone; from the everyday heroes, the artists and scientists and philosophers), to innovators and social entrepreneurs, to the brilliant engineers creating disruptive and catalysing technologies, to the game changing humans solving global development challenges.

Throughout my career I’ve been fortunate to work on a number of ground-breaking, innovative projects around the world and had the privilege of working with exceptionally talented and passionate people; from academia, to non-profits & social enterprises, to tech startups. For a long time I worked in universities and research labs as an astrophysicist & “big-data” research consultant. I moved back to Australia at a time when Melbourne's tech industry began thriving, and it's been really exciting to see how it's grown. There is nothing more satisfying than working with amazingly talented people, and so I try to work on as many collaborative projects as possible. I often wear many hats and seem to straddle many boxes. So far it’s served me well and enabled me to give back to others. My hope is that these experiences will one day enable me to solve any and all problems thrown at me.

The idea of combining data, design, and machine learning to build intelligent products and services to improve people’s lives excites me most these days. I’m currently pursuing compelling data science roles, that have a splash of human-focussed design thrown in for good measure. Why data science AND design? The most ambiguous, complex problems are arguably the most interesting, and I’m more than comfortable with that. You could say it’s my natural state. Human–centred design focuses very much on drilling down to the real problems people face, and by extension their businesses; so being able to deal with ambiguity while you figure out exactly what the problem is, why you’re doing it and who you’re doing for, is and incredibly good skill. Most data has very little meaning until you understand its context, understand how it was created, and why it was collected. Design methodologies help you ask the right questions and with the right data.

So here’s my advice for the aspiring data scientist. Science the crap out of your data. If you don’t have a research background learn about the scientific method. Think like a scientist & business analyst. Work on problems that matter. Be critical of data & algorithms. Learn to ask the right questions; to different types of people, and in many different ways. Incorporate design methodologies. Be as creative as you can. Learn how to prototype solutions quickly. Develop metrics that tell you that you’re on the right track. Better still, show people. Build fast but don’t sacrifice quality. Question your assumptions – constantly. Question other people’s assumptions. Approach data with a healthy dose of skepticism. Co-create if you can. Iterate, iterate, iterate! You’re never going to get it right the first time around.

Understand the systematics and biases in your data. Understand your own personal biases – they will affect your choices and they will influence others. If you don’t think you have any, look harder. You will find them. Learn as much as you can from designers, business analysts, product managers, and software engineers. Learn from people who have a completely different perspective, and completely different experiences. Be kind. Be ethical. Be awesome. Advocate for responsible AI. Encourage diversity of thought & experience.



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