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.
Your Stakeholders Can't Wait 10 Years
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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 previous five years fitting GLMs. That was sufficient to build a model that reduced the number of complaints received about one aspect of the pricing process to zero. Around age 39, having just completed a Masters in Machine Learning and AI, I moved into a role as a data science technical specialist. The AI boom was still a few years off, but cloud-based AI APIs were starting to emerge. I understood the theory behind AI, but this was the first time I'd ever used AI technologies in practice. Yet, I knew enough to be able to piece together an AI data enrichment pipeline that saved hours of manual work for my team. Here's the thing... At the time I took on each of these jobs, I didn't think I was good enough. There was so much more for me to learn. Yet, I still managed to deliver meaningful results without having mastered everything. Because stakeholders are desperate for solutions NOW. They don't have 10 years to wait. Create value with what you know first. Build additional skills when that's not enough. 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.
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...
Building a data science model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else entirely. There’s no room for the kind of errors that might be acceptable in research settings or even standard business applications. And if you get it wrong, you get emails… Lots of emails… From people all around the world… The type of emails that begin with: “how dare you you make my team lose?!” Worse still? You...