Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
Creating Global Impact Isn't About Location
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Back in my PhD days at the Australian National University, I dreamed of making an impact on the world through my work as a statistician and data scientist, but struggled to imagine how that was even possible from a city as small and remote as Canberra. Down the hall from me, one of my PhD supervisors had recently started analysing cricket data. Although this was, without a doubt, the most interesting use case being explored by any of my colleagues, getting an international sports body like the ICC to notice this work seemed like an impossible dream. Fast-forward over a decade to about a month ago, when I was doing a newspaper quiz with my parents and up popped a question about the Duckworth-Lewis-Stern cricket scoring system. “Hey, is that the Stern you used to work with?” asked my mum. I was so proud to be able to say, “Yes, it is.” Which got me wondering: how does a statistician working on a technical problem in a small city in Australia achieve the seemingly impossible goal of having their solution adopted by sports governing bodies around the world? In the 2025 return of Value Driven Data Science, Prof. Steve Stern joins me to answer this question. Steve shares how he transformed a mathematical critique of a cricket scoring system into becoming the custodian of the globally adopted Duckworth-Lewis-Stern method - all from an office in Canberra, Australia. This episode reveals:
Steve’s story proves that creating global impact isn’t about being in the right place - it’s about understanding how credibility and trust are built in high-stakes domains. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 94: Creating Global Impact with Data Science Talk again soon, Dr Genevieve Hayes |
Data Science Impact Algorithm
Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
Around age 19, having just graduated high school, I got my first job in "data". I could barely use Excel and thought Python was something you'd find at the zoo, but a friend of the family hired me to tutor their teenaged son. My maths was good enough to help him get through high school maths. Around age 29, having just finished my PhD, I landed a role managing an insurance pricing and analytics team. I'd never heard the term "machine learning" back then, but I had spent a good chunk of the...
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