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 Data Science Meeting Everyone Actually Looked Forward To
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One of my proudest moments as a data science manager was starting a team coding club. As a data science manager in the early days of Python adoption, I faced a practical dilemma: my team needed coding skills, but nobody could face another mandatory training session. So I tried something different. Project Euler coding problems. One challenge per week. Solve it first individually, then we'd share solutions as a group. We took turns to bring along snacks. What happened next was a huge surprise. Our weekly meetings - filled with equal parts coding breakthroughs and chocolate biscuits - became the highlight of everyone's schedule. The coding skills came naturally. The team dynamics was the real prize. Here's the thing... Sometimes the fastest way to increase your impact and influence is through building the kind of team culture that gets noticed by senior leadership. Whether you're managing people now or aspire to later, creating shared learning experiences can become your secret weapon for career advancement. By the time I left that role, my team was legendary for their collaboration. All because we chose to learn together, one chocolate biscuit at a time. Talk again soon, Dr Genevieve Hayes First published: July 6, 2025 |
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...