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
|
“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.
The first thing I ever published that attracted any real attention was just after I finished my PhD. I started writing the puzzle page for Actuaries Magazine as a way of filling my suddenly empty weekends. I didn’t expect it to lead to anything. Yet, in the years that followed, people would walk up to me at conferences and want to shake my hand because I was “the puzzle girl”. Once someone even offered me a job because of it. Prof Rob Hyndman, one of the world’s leading applied statisticians,...
Even at the best of times, the world is an uncertain place. Right now, we are far from the best of times. In periods of high uncertainty, people naturally seek out anything that might reduce that uncertainty - even just a little. Politicians understand this instinctively, which is why we've seen so many world leaders addressing their people in recent weeks, with varying degrees of success. The greater the uncertainty, the greater the value of any reduction in it. This principle doesn't stop...
When I started my career, data science didn’t exist as a field. I trained as an actuary and statistician and those were the tools I relied on in my earliest roles. Then, around 10 years ago, I started hearing about the wonders of machine learning and became worried that my traditional training was no longer enough. So, despite already having a PhD in Statistics, I went back and completed a Masters in Machine Learning. Then came the AI wave – ChatGPT, large language models, generative AI – and...