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.
Bootcamps Teach Python - They Don't Teach This
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Data science bootcamps teach Python. They don’t teach you how to turn chaos into answerable questions. To this day, whenever I’m faced with a problem, the first thing I do is come up with a series of research questions - then I try to answer them. It’s standard practice in academic research, but I’ve rarely seen it done elsewhere. That experimental approach keeps me focused on what actually moves the needle. And it’s the kind of strategic thinking that gets you noticed by senior stakeholders. Last week, I wrote about the brutal reality of transitioning from academic to industry data science. This week, I’m flipping the script. Academics bring powerful skills to industry that many data scientists never develop. Skills like:
In the latest Value Boost episode of Value Driven Data Science, Dr. Sayli Javadekar returns to explore these transferable skills - and how any data scientist can develop them. You’ll learn:
Academic background or not, these skills will set you apart. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 93: What Industry Data Scientists Can Learn from Academic Training Talk again soon, Dr Genevieve Hayes. p.s. I am going to be taking a break for a few weeks but will return for 2026 in February. Merry Christmas and a Happy New Year! 🎄🎁 |
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...