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 Missing Apprentices
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Recently, I've been systemising my workflows. It's partly about efficiency, but also a way of experimenting with AI. Whenever I encounter a process I've performed dozens of times, I look for a way to automate, templatise or standardise it by feeding my existing knowledge and artefacts into Claude. It's system leverage 101, turbo-charged by AI. But even though AI makes this easier than ever before, none of this would be possible if I hadn't already mastered these tasks. I'm not asking AI to perform tasks I don't understand. Instead, I'm using it to amplify my existing expertise. Here's the thing... I only have this expertise to begin with because I once did the routine work that AI can now systemise. Graduates have traditionally built expertise through repetitive, knowledge-based tasks. Yet these are exactly the tasks AI excels at. This is why entry-level roles are becoming increasingly exposed. For now, organisations can leverage the expertise their mid-career professionals have already developed. But what happens ten years from now? With fewer "apprentices" entering the pipeline, there will be less expertise to leverage, and a short-term gain becomes a long-term problem. Building expertise, in ourselves and in those who follow, is an investment in the future, not a cost to be automated away. Leverage can't create value from nothing. It can only amplify what's already there. 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.
If you want to build a physical structure, be it a mine, a bridge, or a factory, the first thing you invariably need to do is create a blueprint. You can’t just show up at the construction site and start building, or you risk wasting a lot of time and money on a project that returns nothing more than a structurally unsound pile of raw materials. The same is true when building a digital twin. But here’s where data scientists have one major advantage over their counterparts in the physical...
A few years back, I did a piece of data analysis that became the basis of expert testimony in a court case. I was even cross-examined under oath. I understood the gravity of the work right from the start, so I was extremely careful to document and check everything I did. Nevertheless, I was surprised by some of the things that were subpoenaed, in particular scanned copies of the handwritten notes I'd made during meetings with the people who were instructing me. What opposing counsel wanted to...
Whenever I go to a new restaurant, for the first few visits I’ll order a different meal every time. Sure, one of them might turn out to be a disaster, but the cost of failure is low and it’s the easiest way to determine which menu items I like the most. This same experimental approach works very well for most of the decisions we encounter every day in our lives and in our work. But what about the big decisions that land on executives’ desks, where the cost of failure is high and there’s no...