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.
The First Rule of Machine Learning...
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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 latest Value Boost episode of Value Driven Data Science, Dr Tim Varelmann joins me again to explain how building from first principles creates a low-cost scaffolding that makes your ML work more robust. In just 11 minutes, you’ll learn:
The first rule of machine learning? Don’t start with machine learning. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 97: Mathematical Modelling as a Gateway to ML Success 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.
“Analyse this dataset and come back with any insights you find.” This was one of the first requests I ever received as a data scientist, and to be perfectly honest, I didn’t quite understand what it meant. Sure, I understood the “analyse the dataset” part, but at the time, I had no idea what my stakeholder meant when she spoke of “insights”. I interpreted the word as a synonym for “interesting facts”. And that’s exactly how she politely responded when I returned with my findings. “That’s...
“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,...