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
|
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.
Last month, Anthropic CEO Dario Amodei published an essay calling for stronger AI regulation and a slower pace of AI development. Since then, two incidents have made his case for him. In the first, the US military almost started a war based on the contents of a confidently wrong AI-generated intelligence report. In the second, OpenAI disclosed that its agent hacked into Australia's Medicare website during training and accessed data that, although not sensitive, had yet to be publicly...
I once met a data scientist who was so good at his job that he automated his way out of it. He systematically built automations to handle his team’s work and once those automations were in place, the team no longer needed as many people. So, his role was ultimately made redundant. He’d built those automations because he believed they’d create business value for his employer - and they did. The problem was that the value ended up residing in the code, not in him. And because his employer owned...
The hardest course I took during my entire data science education was a theoretical computer science class. But not for the reasons you'd think. Theoretical computer science is basically pure maths for data scientists, and the material was genuinely challenging. I spent 30 hours/week studying just to keep up. Yet, the reason why this course became known as "the widow maker" wasn't just the time commitment. It was because referring to any materials beyond the prescribed texts was considered an...