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 Sum of Their Choices
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Organisations live or die based on the quality of the decisions made by their executives. So, if you’re a data scientist looking to create value, the answer is simple: help your senior stakeholders make better decisions. This means you need to understand the decisions your stakeholders are trying to make. Any analysis you do needs to connect back to those decisions at the end. Machine learning, data analysis, all the technical work people associate with data science - that’s just one step in the process, not the whole process. It’s an essential step. But without the steps that connect your work back to decisions, the value you create is massively reduced. As Luther Stickell (Ving Rhames) says in Mission Impossible - The Final Reckoning: “Our lives are not defined by any one action. Our lives are the sum of our choices.” Your stakeholder’s career is the sum of their decisions. Make sure your work actually helps them make better ones. 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.
"Because the algorithm said so” isn’t good enough anymore. When your machine learning model makes a decision that affects someone’s medical treatment, financial security, or legal rights, stakeholders need to understand why. I first encountered interpretable machine learning working in insurance - though I didn’t realise it at the time. The insurer I worked for used machine learning models as part of its premium calculation process. There was an unwritten rule that any models we deployed had...
Data scientists love to jump straight to machine learning. New problem? Throw data at a neural network. See what happens. But there’s a foundational step that, in the right circumstances, can dramatically increase your chances of project success - and most data scientists skip right over it. Mathematical modelling from first principles. I’m talking about physics here. Mass conservation. Energy conservation. Newton’s laws of motion. The stuff you find in high school physics textbooks. In this...
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...