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.
It's Not AI that will Take Your Job
|
Each week, it seems like there’s yet another announcement of technical workers losing their jobs to AI. In Australia, for example, tech giant Atlassian recently laid off 1,600 workers - 10% of their global workforce - “to steer more spending into AI”. Now, granted, not every AI-related job loss is necessarily as it first appears. Some experts point to AI-washing. That is, companies using AI as cover for restructuring decisions they would have made anyway. But regardless of the reason, the reality is that AI has fundamentally changed the nature of white-collar work. And for technical workers, including data professionals, it raises the uncomfortable question: if AI can do what you do, why would anyone need you? The solution, according to Tim Dietrich, isn’t to compete with AI. It’s to harness it as a force multiplier - expanding what you can deliver, taking on work you couldn’t have touched before, and making yourself not just relevant, but indispensable. As technical professionals experienced in working with cutting-edge software tools, we are better placed to do this than most. Tim is a software developer with over 25 years of experience. Until about nine months ago, he described himself as “the world’s most negative person on AI”. He was convinced it was overhyped and quietly hoping he could make it to retirement before it affected his work. He didn’t make it that far. But what he built instead is far more interesting. In the latest episode of Value Driven Data Science, Tim joins me to share how he built a virtual team of over 100 AI specialists that allows him to operate with the output and breadth of a small firm - while remaining a team of one. You’ll discover:
The question isn't whether AI will change your work. It's whether you'll be ahead of that change or behind it. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 107: Building a Virtual Empire of AI Specialists 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.
Although Python is my usual weapon of choice, I'm not averse to building the occasional model in Excel when the use case fits. Having originally trained as an actuary, there was a point in my life when I literally lived and breathed Excel. It still feels like second nature - which is probably why I've never felt tempted to use AI assistance when building Excel models, despite using it when writing code. However, after speaking recently to a fellow data professional about how much AI struggles...
When I’m looking for guests for my podcast, the first place I turn is invariably my bookcase. The authors of data science books have already demonstrated their authority in the field to the point of developing their own original IP. That makes them ideally suited to a podcast about data science expertise. And it’s no coincidence that many of those authors started off by writing blogs. It turns out conference organisers do something similar. When Cynthia Dunlop, who helps organise two major...
A couple of years back, I watched a TV series called The Fear Index. The series focused on a hedge-fund manager who had developed an AI system to optimise fund profits. Of course, as you would expect for a TV show - 🚨Spoiler Alert🚨 - everything falls apart when the AI starts taking highly illegal and frequently fatal actions in order to drive the market and achieve its goals. I enjoyed the show immensely, but at the time, felt it was far-fetched. However, recent reports of an OpenAI agent...