Growing up, my parents had arts degrees and couldn’t understand a word I said about maths or science. Every dinner conversation went something like this: My parents: “What did you learn at school today?” Me: “In maths, we learned about differential equations.” My parents: 😕 “So… is that a good thing or a bad thing?” At the time, I thought this was incredibly frustrating. However, I now realise it was the best training I could have gotten for my data science career. Because when you spend...
5 days ago • 1 min read
I’ve made three major career pivots in data science. None of them involved climbing a ladder. I thought academia was going to be my forever job. I imagined being in a university until the day I retired - possibly until I died, because academics last forever. Then I got to the end of my PhD and realised: I don’t actually want to spend the rest of my life in school. So, I made my first career pivot - from academic to insurance pricing manager. A few years later, I heard about this exciting new...
8 days ago • 1 min read
When it comes to building a career, every data scientist is running their own business - it’s just that most of those businesses are solo operations with one client: their employer. Think about it. To succeed in data science, you need to: Market your skills internally; Find opportunities for new projects; Manage stakeholder relationships; Deliver value and try to ensure repeat “customers” (in the form of more interesting work). The are all skills required to succeed in solo consulting. The...
12 days ago • 1 min read
My zombie apocalypse binge taught me something about data science: Sometimes you need to break up with your favourite technique. This Friday is Halloween, which, being a life-long horror nerd, I would normally treat as an excuse to write a post connecting data science to horror movies (as I did in 2024 and 2023). However, after binge-watching way too many seasons of The Walking Dead, I started to feel like I was trapped in my own apocalyptic nightmare. The endless cycle of zombies, violence...
15 days ago • 1 min read
Most data scientists think the hardest part of experimentation is the statistics. It’s not. It’s telling people their ideas didn’t work. Here’s a reality check about experimentation: Even at companies like Google and Netflix, 70-90% of experiments don’t show positive results. That means if you’re running A/B tests, you’ll be delivering “bad news” far more often than good news. Now imagine being the data scientist who constantly tells people their ideas didn’t work. How long before...
19 days ago • 1 min read
"The show doesn't go on because it's ready; it goes on because it's 11:30." - Lorne Michaels, creator of Saturday Night Live. Data scientists can learn a lot from Saturday Night Live. SNL has a rule: The show goes on at 11:30. Not when it’s perfect. Not when everyone’s happy with it. At 11:30. Many years ago, I was responsible for performing the annual workcover premium rate calculation for the whole of Victoria. It was a calculation of the utmost importance - $2b in revenue depended on it...
22 days ago • 1 min read
For the 4 1/2 years of my PhD, I worked with a de-identified dataset that felt like nothing more than numbers on a page. Cold. Abstract. Disconnected from any real human experience. Each “person” was just a line in an Excel spreadsheet, with an ID in place of a name. When I started my first role in insurance pricing, my mindset initially remained the same. That was until my boss took me along to speak to a policyholder - putting me face-to-face with one of the people my data actually...
26 days ago • 1 min read
12 years in government taught me something surprising about data science. Making money and making an impact aren’t always the same thing. The easiest way to create value as a data scientist is to help your organisation to make more money. After all, everyone wants more money, don’t they? As Elon Musk’s recent $1 trillion pay deal suggests, even the richest person on Earth. Yet, while money is valuable, money and value aren’t necessarily the same thing. And if you work for a not-for-profit or...
29 days ago • 1 min read
Data science requirements gathering is about as popular with stakeholders as vegetables are with kids. The solution is also the same... Most data scientists dread these sessions. For stakeholders, the experience is probably far worse: Conflicting voices talk past each other. Senior executives dominating discussions. Junior team members too scared to speak. Political dynamics derailing productive conversation. The result is invariably requirements that miss the mark and doom projects from the...
about 1 month ago • 1 min read