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.
Value Driven Data Science is (Almost) Back
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Data science in the real world is about more than just building models - it’s about building models that work under pressure, earn trust, and avoid costly mistakes. Seems obvious, doesn’t it? Yet most data science education skips right past these fundamentals. Bootcamps teach you how to code algorithms, not how to prevent bias. University courses cover model architectures, not how to build systems that perform when millions of people are watching. No wonder most data science models never get deployed. That’s a gap Value Driven Data Science exists to fill. This year marks the fifth season of Value Driven Data Science - five seasons of conversations with some of the smartest people working at the intersection of data, decisions, and strategy. And we’re just getting started. Season five launches on Thursday, 19th February and here’s a sneak peek at some of the topics and guests we have lined up:
Oh, and one more thing… Episode 100 is just around the corner and I have something extra special planned. I’ll share more details closer to the release. If you haven’t caught up with Value Driven Data Science yet, you can listen to the first 93 episodes HERE or find it on Apple Podcasts, Amazon Music or Spotify. And if you’re already a fan of the show, please consider leaving a rating or review on your favourite podcast platform so that more people can find it. 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...