The most valuable lessons I’ve learned in my data science career weren’t learned in a classroom. They came from conversations with people who’d already figured things out the hard way. My podcast has been a more valuable learning tool for me than all of my university degrees combined. Over 100 episodes, I’ve had the chance to speak one-on-one with some of the sharpest minds in the industry - CEOs, best-selling authors and leading researchers - on everything from cutting-edge AI to what it...
6 days ago • 2 min read
In 2015, I fell in love with a job I would never have. I’d just attended a conference where people were talking about machine learning and data science as the way of the future. I returned to the office eager to learn more and started down the data science rabbit hole - where I stumbled across an article about the recently established NYC Mayor’s Office for Data Analytics. They were using data science to locate illegal cooking oil dumping in the city’s sewers. To coordinate emergency services...
10 days ago • 1 min read
The first time I ever presented my work in public was at a finance symposium when I was 27. I was close to submitting my PhD thesis and my supervisor offered me the opportunity as a supporting speaker to a renowned international mathematical finance researcher. I was the final speaker of the day. By the time I took the podium, almost everyone had gone home. Fewer than 10 people remained in the room. But the researcher was still there. Afterwards, I headed to the airport, and ran into him in...
20 days ago • 1 min read
Biased machine learning models don’t just produce poor predictions. They damage reputations, derail projects, and in high-stakes fields like healthcare, they can potentially cause real harm. Yet most data scientists don’t check for bias until it’s too late - missing the opportunity to address it at its source. Serg Masis, author of Interpretable Machine Learning with Python, puts it bluntly: “Models magnify bias just simply by the way they are. It’s like when you make a caricature of someone...
24 days ago • 1 min read
“Cheating with artificial intelligence is now rampant at universities.” “University is no longer a test of your intellect. It’s a test of how well you can instruct Chat GPT.” “AI Is giving students top grades for zero intellectual work.” These are quotes from a recent article in The Australian Weekend Magazine, which argues that students are now turning to AI en masse to automate learning, and graduating with perfect grades but limited knowledge. The phenomenon has been observed across...
27 days ago • 1 min read
"Because the algorithm said so” isn’t good enough anymore. When your machine learning model makes a decision that affects someone’s medical treatment, financial security, or legal rights, stakeholders need to understand why. I first encountered interpretable machine learning working in insurance - though I didn’t realise it at the time. The insurer I worked for used machine learning models as part of its premium calculation process. There was an unwritten rule that any models we deployed had...
about 1 month ago • 1 min read
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...
about 1 month 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...
about 1 month 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...
about 1 month ago • 1 min read