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.
Originality is Overrated (in Data Science and in Business)
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I recently met a brilliant data scientist who had built some amazing AI automation tools. But he kept saying things like “it’s not very original” and “I’m not building anything new, I’m just piecing together what’s there.” And I told him: “Your solutions don’t have to be original. They just have to solve the problem.” This is something I see all the time with technical people. They think they need to invent something revolutionary to be valuable. Here’s the thing… Your stakeholders don’t care if your solution is original. They care if it works. They don’t need you to reinvent the wheel. They just need you to use the wheels that exist to get them where they want to go. Some of the most successful businesses in the world are just better implementations of existing ideas. Uber didn’t invent taxis. They just repackaged ideas in a way that people found to be of value. Stop apologising for building practical solutions with existing tools. Start focusing on the value you create. 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.
Like most data scientists, I started my career in traditional 9-to-5 roles. With bills to pay and limited experience, a regular job felt "safe". Then COVID hit. Nothing felt "safe" anymore, and my dreams of working for myself no longer seemed so insane. I finally made the leap - and never looked back. Many data scientists share similar dreams of breaking free from traditional employment, but don't know where to begin. Yet, here's what I've learned along the way: there's no single path to data...
A data scientist I know once told me of a manager who would end every team meeting with the same advice: “keep kicking goals.” What does that even mean? Which goals? With what resources? How do you even measure what a goal is? Unless you’re actually talking to a football team, “keep kicking goals” is meaningless. Yet this is what some data scientists face. Well-meaning managers who say “go do data science” without giving direction on what that actually requires. And then they wonder why...
Want to know the book that's had the greatest influence on my thinking as a data scientist? I bet you'll never guess. It's Jurassic Park (the book, that is - definitely NOT the movie). As a teenager, I read everything I could find by Michael Crichton and his message of focusing on solving the problem while ignoring all irrelevant distractions still influences the way I work. Whenever I feel overwhelmed in my work, that's what I remind myself. I've been doing it for decades and will do it for...