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.
Three Ways to Fail as a Data Scientist (Only One is Technical)
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There are three ways to fail as a data scientist. Only one is about technical skills.
If you’re not solving the right problems, then you have a opportunity identification issue. If you’re solving the right problems, but producing the wrong results, then you have a competency issue. If you’re solving the right problems and producing correct results, but nobody knows, then you have a positioning issue. These are all problems that need to be addressed - and opportunity identification and positioning issues are much easier to fix than technical competency. Yet, so few data scientists take the time to do so. I recently spoke to a data analyst who was disappointed that her stakeholders thought her custom reports were created simply by pushing a button. My response was: “You should take that as a compliment. It means that you did your job so well that people think it’s that easy.” Because here’s the thing… If your stakeholders think what you do is simple, that’s actually a sign you’re solving the right problem and doing it right. You’ve made a complex, high-value solution look effortless. The problem isn’t your competency or the value that you bring. The problem is that you’ve made yourself invisible. Many data scientists I know are struggling with a positioning problem. They’re producing great results, but nobody knows it. The solution? Start talking about your work. Share what you’ve accomplished. Explain the business impact. Your work is too good to go unnoticed. 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.
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...
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...