Organisations live or die based on the quality of the decisions made by their executives. So, if you’re a data scientist looking to create value, the answer is simple: help your senior stakeholders make better decisions. This means you need to understand the decisions your stakeholders are trying to make. Any analysis you do needs to connect back to those decisions at the end. Machine learning, data analysis, all the technical work people associate with data science - that’s just one step in...
5 days ago • 1 min read
Data scientists love to jump straight to machine learning. New problem? Throw data at a neural network. See what happens. But there’s a foundational step that, in the right circumstances, can dramatically increase your chances of project success - and most data scientists skip right over it. Mathematical modelling from first principles. I’m talking about physics here. Mass conservation. Energy conservation. Newton’s laws of motion. The stuff you find in high school physics textbooks. In this...
9 days ago • 1 min read
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...
12 days ago • 1 min read
You’re already using optimisation every day as a data scientist - but you probably don’t think about it. Every time you train a machine learning model, you’re running an optimisation algorithm to find the best parameters. But there’s a second type of optimisation that most data scientists never even touch - and it’s where the real business value often lives. It’s called decision optimisation, and it can transform your ML predictions into actionable decisions. Here’s the difference: ML...
16 days ago • 1 min read
Data science exists to support decision making, but within any organisation, there is a hierarchy of decisions - low stakes, high volume decisions at the bottom; high stakes, low volume decisions at the top. Executives care most about the decisions at the top. There’s valuable work at the bottom of that hierarchy. But automating routine decisions is about clearing the path so executives can focus on the high-stakes decisions. It’s not about walking it with them. For years, data science has...
19 days ago • 1 min read
Building a data science model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else entirely. There’s no room for the kind of errors that might be acceptable in research settings or even standard business applications. And if you get it wrong, you get emails… Lots of emails… From people all around the world… The type of emails that begin with: “how dare you you make my team lose?!” Worse still? You...
23 days ago • 1 min read
Data science skills are like gold. And that's not necessarily a good thing. Think about it... A gold bar is very valuable. At the time of writing this, gold is selling for around US$5k/oz and (US) gold demand more than doubled in 2025. Similarly, specialist data skills, such as data science and data engineering, are also very valuable. Data roles typically command above-average salaries, and data skills are frequently included in lists of the highest-demand skills. Yet, both gold and data...
26 days ago • 1 min read
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...
about 1 month ago • 1 min read
This morning, while listening to the radio, I heard the following advice. It was directed at politicians, but could just as easily have applied to data scientists: “If they want to gain the support of the public, they need to spell out their priorities and explain what they plan to do to address them.” The commentator’s point was simple - politicians often lose public support not because their policies are bad (although this may be the case), but because they fail to communicate: What they’re...
about 1 month ago • 1 min read