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.
Beyond Writing Code
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“I haven’t written code by hand in months - and honestly, I don’t want to anymore.” This admission came from one of the most capable data scientists I know. Until recently, he was shipping enterprise-scale code at a top multinational company - without writing a line of it himself. He now builds cutting-edge AI tools for small businesses. For him, understanding the architecture, logic and business context was enough. His ability to hand-code was slowly atrophying, but something new was growing in its place. His story reminded me of a conversation I’d had with a CFO, almost a decade earlier. She trained as an accountant, but as she climbed the corporate ladder, her ability to perform accounting work had quietly faded. What remained - and what mattered - was her ability to read financial statements and understand what they meant for the business. Two conversations. Ten years apart. The same pattern. Here’s what I think this indicates… Both the data scientist and the CFO succeeded because of their technical foundations - not in spite of having moved beyond them. Their knowledge didn’t disappear, but it changed shape - from something you do to something you think with. The deep technical skills that previously defined data science, such as coding, model fitting and algorithm selection, may be heading the same way. But what’s replacing those skills isn’t nothing. It’s judgement - knowing when to reach for a tool, why it works and what the output actually means for a business. And increasingly, it’s the ability to communicate that judgement clearly to people who don’t share your technical background. Data scientists are becoming managers of AI systems. And that means the skills that used to be the hallmarks of leadership, such as strategic thinking, clear communication and business fluency, are now essential capabilities for everyone in this field. Most data scientists weren’t trained in them. Few are actively developing them. This is where the opportunities lie. The time to develop them is now. 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.
In a recent blog post, CFO advisor and friend of the list Lauren Pearl discussed the impact of AI on the age-old software “build vs buy” decision. In short, Lauren argued that AI-driven vibe coding has made building software in-house far more appealing when the stakes are low, but when stakes are high, buying is still the way to go. To quote Lauren: “When you buy software, you don’t just buy the code. You buy a support team, expert-derived flows, tested features, platform trust, regular...
Ten years ago, everyone wanted to be doing machine learning. Go to a data conference - someone was presenting on ML. Advertising a data role - just mention ML and applications would 10X. It was the promise of machine learning that inspired an entire generation of data scientists. I know. I was one of them. And then came the statistic that revealed what many data scientists knew but few wanted to admit: around 90% of ML initiatives never made it to deployment. The ideas were there. The...
Last week, I stepped on the fancy scales at my gym for the first time in four months. You know the ones. They don’t just tell you your weight. They tell you your muscle mass, bone density, basal metabolic rate… Basically, everything short of your star sign. The results were horrifying. Everything had gone backwards. I’d even managed to lose 400 grams of muscle. I’d been training five days a week and made no major changes to my diet. I was lifting heavier weights than ever before. None of it...