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.
Who Owns the Value You Create?
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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 code, he was the one considered dispensable, not the tools he’d built. It’s an old story - it occurred well before the current AI wave - and it did have a genuinely happy ending, in that he ultimately built a business around code-based automation. However, I was reminded of it when business strategist and start-up CFO advisor Lauren Pearl shared a similar story in a recent blog post - a story about generative AI, chicken nuggets, The Wire, and who actually owns the value when someone builds something great. Right now, data scientists are rushing to develop AI agents, in the hope of creating business value. But building something valuable with AI doesn’t automatically make you valuable. In the latest episode of Value Driven Data Science, Lauren joins me to explore where real expertise actually lives in the age of AI and what data scientists should really focus on to build a sustainable career. You’ll discover:
Building AI tools is a good start. Building expertise is what makes you irreplaceable. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 125: The AI Chicken Nugget Problem 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.
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...
Suppose you were screening for a rare medical condition that occurs in just 1 in 1,000 people. The test you're using is highly accurate, with a true positive rate of 99% and a true negative rate of 99%. A person tests positive. What is the probability they actually have the condition? The answer, which can be verified using Bayes' theorem, is just 9.02%. That means it's more than 10X more likely that a person who tests positive has received a false positive result than that they actually have...