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.
Statistics is Dead. Long Live Statistics.
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When I started my career, data science didn’t exist as a field. I trained as an actuary and statistician and those were the tools I relied on in my earliest roles. Then, around 10 years ago, I started hearing about the wonders of machine learning and became worried that my traditional training was no longer enough. So, despite already having a PhD in Statistics, I went back and completed a Masters in Machine Learning. Then came the AI wave – ChatGPT, large language models, generative AI – and that seemed like the next frontier to pursue. Each step felt like it was taking me further from my statistical roots. But then something unexpected happened. People started approaching me for projects specifically because of my statistics background. Projects with too little data to train a neural network, but where a classical statistical model was a perfect fit, or where rigorous statistical analysis was the right answer rather than a predictive model. It slowly dawned on me that ML and AI weren’t the silver bullet I’d come to believe they were. Rather than being the next steps in the evolutionary chain beyond statistics, they were merely two additional tools that could exist alongside it. I came to this realisation relatively recently and somewhat by accident. Prof Rob Hyndman, one of the world’s most influential applied statisticians, never needed to. In the latest episode of Value Driven Data Science, Rob joins me to make the case for why rigorous statistical thinking remains indispensable in the age of AI, and what data scientists are giving up when they abandon it. In this episode, you’ll discover:
Rob never stopped believing in classical statistics. After this episode, you won’t either. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 101: Why Traditional Statistics Still Matters in the Age of AI 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.
“I haven’t written code by hand in months - and honestly, I don’t want to anymore.” This admission came from one of the most capable data scientists I know. Until recently, he was shipping enterprise-scale code at a top multinational company - without writing a line of it himself. He now builds cutting-edge AI tools for small businesses. For him, understanding the architecture, logic and business context was enough. His ability to hand-code was slowly atrophying, but something new was growing...
The first thing I ever published that attracted any real attention was just after I finished my PhD. I started writing the puzzle page for Actuaries Magazine as a way of filling my suddenly empty weekends. I didn’t expect it to lead to anything. Yet, in the years that followed, people would walk up to me at conferences and want to shake my hand because I was “the puzzle girl”. Once someone even offered me a job because of it. Prof Rob Hyndman, one of the world’s leading applied statisticians,...
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