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.
One of the most frustrating things about working with AI is when you get to the point where it seems like Claude or ChatGPT or your AI tool of choice really understands you and the way you work - and then all of a sudden it doesn’t. It’s like training a new graduate to be useful at their job, only to be told they’ve moved to another role and you have to start again, training someone else. This is annoying enough when dealing with AI chatbots. But, when building AI systems, it can be...
Earlier this month, Anthropic launched the Claude Frontier Academy, a program designed to train engineers to change how businesses run using agentic AI. The announcement reads as a clear signal that AI models alone aren't enough to create business value and that people are still essential for this technology to succeed. I couldn't agree more. The program's target profile makes this point even clearer. Anthropic wants engineers with strong technical skills, but also "experience helping others...
The first course I had to take as part of my computer science Master’s was a compulsory course on algorithms. Regardless of whether you were a data scientist, like myself, specialising in machine learning, or a software engineer specialising in computer systems, everyone had to take it - and for good reason. Because algorithms are the building blocks of everything technology professionals do, much like how protein bases are the building blocks for all things biological. The AI era hasn’t...