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.
ML is Just One Piece of the Puzzle
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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 Decision Factory, probability is one of the most overlooked layers in most decision systems and its absence leaves organisations making decisions as if all future uncertainty could be collapsed down to a single number, when that couldn’t be further from the truth. Probability is just one of eight disciplines Adam believes are necessary to build decision systems that actually work. And most data scientists are only fluent in one or two of them. In the latest Value Boost episode of Value Driven Data Science, Adam joins me to walk through the eight disciplines that make up the modern decision stack and where data scientists should focus to close the gaps in their own skill set. In just 12 minutes, you'll discover:
Machine learning is what most data scientists know best. The other seven disciplines are where the opportunities lie. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 124: 8 Disciplines Every Decision System Needs 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...
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...