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
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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.
I once met a data scientist who was so good at his job that he automated his way out of it. He systematically built automations to handle his team’s work and once those automations were in place, the team no longer needed as many people. So, his role was ultimately made redundant. He’d built those automations because he believed they’d create business value for his employer - and they did. The problem was that the value ended up residing in the code, not in him. And because his employer owned...
The hardest course I took during my entire data science education was a theoretical computer science class. But not for the reasons you'd think. Theoretical computer science is basically pure maths for data scientists, and the material was genuinely challenging. I spent 30 hours/week studying just to keep up. Yet, the reason why this course became known as "the widow maker" wasn't just the time commitment. It was because referring to any materials beyond the prescribed texts was considered an...
Before I was a data scientist, I was a statistician. For a long time, I thought of that as a liability. Machine learning felt like the way of the future and my statistical training felt like it belonged in the past. But over time, I noticed something. While most data scientists thought in terms of point estimates, I instinctively thought in terms of uncertainty ranges. It turned out that instinct was more valuable than I’d given it credit for. According to Adam DeJans Jr, co-author of The...