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 Price of Getting Good
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A data scientist friend of mine once took a role in a team that never stopped complaining about their difficulties in recruiting technical staff. She had solid data skills, but not the specific organisational knowledge the role needed. Yet, she was willing to learn, and management seemed willing to train her. So, she asked for training. And she asked again. Her boss promised to show her “as soon as an easy project came up.” The work was too important for her to make any mistakes. In the meantime, she was assigned to R&D work. Two years later, she left that role, having never been shown how to log into the system. Her boss was still complaining about not having enough staff. My friend’s boss treated mistakes as a risk to be avoided, when the real risk was never letting her make them at all. Being bad at something is an inevitable part of learning and it’s the only way you can ever become good. It’s painful to live through, and even more painful to manage, but it’s the price you have to pay for expertise. When I was a manager, I applied this principle to my team. And after launching my data science business, I applied it to myself. Four and a half years later, people are now coming to me for advice. This is one of the many things I recently discussed with Taiwo Ash on his podcast, Breakfast and Launch. Check out the full episode HERE. 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...