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.
Is AI Making You Smarter or Just Faster?
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Earlier this year, entrepreneur Mark Cuban posted the following on X: “There are generally two types of LLM users: those that use it to learn everything, and those that use it so they don’t have to learn anything.” As this quote suggests, AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place. Given my expertise took over 20 years for me to build, I’ve been thinking about this a lot lately. How can I use AI to make me better at my work, not just faster? That’s the question I keep coming back to. Tim Dietrich, a software developer with over 100 virtual AI specialists on his team, has been thinking about the same tension. In a recent blog post, he wrote about what he calls the mindful use of AI, after noticing he’d started looking for problems to solve with AI rather than reaching for it when a genuine problem arose. In this Value Boost episode, Tim joins me to explore how to stay on the right side of that line and what mindful AI use actually looks like in practice. In just 10 minutes, you’ll discover:
The goal isn’t to use AI more. It’s to use it in a way that makes you more. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 108: How to Use AI Without Losing Your Edge 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...