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.
Data Science Skills Are Like Gold (And That's Not Good)
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Data science skills are like gold. And that's not necessarily a good thing. Think about it... A gold bar is very valuable. At the time of writing this, gold is selling for around US$5k/oz and (US) gold demand more than doubled in 2025. Similarly, specialist data skills, such as data science and data engineering, are also very valuable. Data roles typically command above-average salaries, and data skills are frequently included in lists of the highest-demand skills. Yet, both gold and data skills are also commodities. Just as one gold bar is indistinguishable from another of the same size and quality, one data professional is often indistinguishable from another with the same technical credentials. Credentials commoditise technical knowledge. The result is the paradoxical situation whereby talented data professionals with some of the most valuable and in-demand skills of the 21st century end up struggling to find work. The solution isn't to become a better commodity. It's to stop thinking like one entirely. Gold bars don't decide how they're used. Someone else determines where they're needed and what they become. Data professionals can decide - but most don't realise the choice exists. Credentials give you knowledge: techniques, tools and methods. But expertise is different. Expertise means understanding your organisation deeply enough to know which business problems are actually worth solving. It means developing judgement about what matters to your stakeholders and then actively proposing solutions - rather than waiting to be told what to build. Technical knowledge is a commodity. Expertise isn't. The technical skills that made you valuable in the first place don't disappear when you make this shift. Instead, they become the foundation of something even more valuable. 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.
Around age 19, having just graduated high school, I got my first job in "data". I could barely use Excel and thought Python was something you'd find at the zoo, but a friend of the family hired me to tutor their teenaged son. My maths was good enough to help him get through high school maths. Around age 29, having just finished my PhD, I landed a role managing an insurance pricing and analytics team. I'd never heard the term "machine learning" back then, but I had spent a good chunk of the...
You’re already using optimisation every day as a data scientist - but you probably don’t think about it. Every time you train a machine learning model, you’re running an optimisation algorithm to find the best parameters. But there’s a second type of optimisation that most data scientists never even touch - and it’s where the real business value often lives. It’s called decision optimisation, and it can transform your ML predictions into actionable decisions. Here’s the difference: ML...
Data science exists to support decision making, but within any organisation, there is a hierarchy of decisions - low stakes, high volume decisions at the bottom; high stakes, low volume decisions at the top. Executives care most about the decisions at the top. There’s valuable work at the bottom of that hierarchy. But automating routine decisions is about clearing the path so executives can focus on the high-stakes decisions. It’s not about walking it with them. For years, data science has...