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 Widow Maker
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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 act of academic misconduct. Every proof we had to complete as part of the course was just a Google search away, so this was the only way the instructor could guarantee we'd learned the work. I understand where he was coming from in creating this rule. The problem was the perverse incentives it created. Students became frightened of doing too well in their homework for fear of claims that they had cheated. Some students even deliberately left in errors, to avoid the inevitable investigation a "perfect" score might trigger. I have never felt prouder than the day I finished that course, but to this day, I'm still not 100% convinced the instructor's approach was educationally sound. Here's the thing... We're seeing this exact same situation playing out again when it comes to AI usage. Schools are banning AI use to make sure students actually learn, which is fair enough on the face of it. But it's also creating AI witch-hunts, where every em-dash is treated as a confession and good students are being accused of cheating for the crime of producing work that seems "too good". LinkedIn recently added a button to report "AI slop" and writers have taken to deliberately leaving in grammatical errors and typos as proof their posts are their own. As a firm believer in the value of expertise, I understand the reasoning behind this. And figuring things out for yourself does force you to learn more than if a technological solution is at hand. Yet, scientific progress has always been made by successive generations of scientists standing on the shoulders of the giants who came before them. AI is a major technological leap for humanity. At some point, we need to stop treating every trace of it as evidence of a crime and start figuring out how to stand on this particular set of shoulders, too. The widow maker didn't just teach students who cheated to fear getting caught. It also taught students who didn't to fear seeming too capable. This time around, we need to be careful about which lessons we're trying to teach. 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.
Last month, Anthropic CEO Dario Amodei published an essay calling for stronger AI regulation and a slower pace of AI development. Since then, two incidents have made his case for him. In the first, the US military almost started a war based on the contents of a confidently wrong AI-generated intelligence report. In the second, OpenAI disclosed that its agent hacked into Australia's Medicare website during training and accessed data that, although not sensitive, had yet to be publicly...
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...
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...