The hardest course I took during my entire data science education was a theoretical computer science class. But not for the reasons you'd think. Theoretical computer science is basically pure maths for data scientists, and the material was genuinely challenging. I spent 30 hours/week studying just to keep up. Yet, the reason why this course became known as "the widow maker" wasn't just the time commitment. It was because referring to any materials beyond the prescribed texts was considered an...
3 days ago • 1 min read
Before I was a data scientist, I was a statistician. For a long time, I thought of that as a liability. Machine learning felt like the way of the future and my statistical training felt like it belonged in the past. But over time, I noticed something. While most data scientists thought in terms of point estimates, I instinctively thought in terms of uncertainty ranges. It turned out that instinct was more valuable than I’d given it credit for. According to Adam DeJans Jr, co-author of The...
7 days ago • 1 min read
Suppose you were screening for a rare medical condition that occurs in just 1 in 1,000 people. The test you're using is highly accurate, with a true positive rate of 99% and a true negative rate of 99%. A person tests positive. What is the probability they actually have the condition? The answer, which can be verified using Bayes' theorem, is just 9.02%. That means it's more than 10X more likely that a person who tests positive has received a false positive result than that they actually have...
10 days ago • 1 min read
If your stakeholders could take two rocks, bang them together three times, spin around, and make a better decision, they would. That’s not a criticism. It’s just the truth about what stakeholders actually want. Your stakeholders do not wake up in the morning hoping that a new predictive model will be waiting in their inbox when they arrive at work. And they do not lie awake at night, wishing that the following day their data scientists will present them with more accurate forecasts. What they...
14 days ago • 1 min read
If you discovered AI had drafted large chunks of a report you'd paid a Big 4 consultancy $435k to deliver, how would you react? The mayor of Wellington recently did just that. But the internet is outraged about the wrong thing. Last November, Deloitte recommended Wellington City Council cut 20% of its staff, with ChatGPT to pick up the slack. However, the analysis subsequently turned out to be flawed. And now it turns out AI wrote the report, too. Based on the way it's being reported, it...
17 days ago • 1 min read
In the first four months of 2026, Uber burned through its entire AI budget for the year. It did so by giving its engineers access to AI tools like Claude Code and then encouraging them to use them “as much as possible”. To make sure their staff got the message, they even set up internal leaderboards to rank staff based on usage. In retrospect, it’s a pretty clear example of what an AI strategy shouldn’t look like. Most organisations today have AI strategies a lot like Uber’s. Their biggest...
21 days ago • 1 min read
A data scientist friend of mine once took a role in a team that never stopped complaining about their difficulties in recruiting technical staff. She had solid data skills, but not the specific organisational knowledge the role needed. Yet, she was willing to learn, and management seemed willing to train her. So, she asked for training. And she asked again. Her boss promised to show her “as soon as an easy project came up.” The work was too important for her to make any mistakes. In the...
24 days ago • 1 min read
When the AI wave first hit, it was all about chatbots, and tech CEOs promised a future where humans and AI would work together to deliver better outcomes than humans could manage alone. Then, as AI progressively became better, businesses started to question whether they needed human workers at all, and the first wave of AI-driven redundancies hit. The tech CEOs suddenly started changing their tune and began warning of the impending white-collar job-pocalypse, especially following the advent...
28 days ago • 1 min read
Data science is obsessed with scalability. Can your model handle bigger data received at a higher velocity than ever before? So, it's ironic that some of the best things you can do for your data science career don't actually scale. Genuinely understanding the problems of a single stakeholder - doesn't scale. Building a reputation as an expert in your domain - doesn't scale. Delivering recommendations that actually get acted on - doesn't scale. AI has made it easier than ever to produce...
about 1 month ago • 1 min read