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.
Data science in the real world is about more than just building models - it’s about building models that work under pressure, earn trust, and avoid costly mistakes. Seems obvious, doesn’t it? Yet most data science education skips right past these fundamentals. Bootcamps teach you how to code algorithms, not how to prevent bias. University courses cover model architectures, not how to build systems that perform when millions of people are watching. No wonder most data science models never get...
Welcome back for 2026! I recently came across two data science services being sold online, both by individual consultants working remotely. The first (on Fiverr) promised: "I will build you a dashboard from your data that meets your specifications." Price: $100 The second was a half-day "check-up" of a company's data capabilities, described as: "suitable for internal use when making strategic choices."Price: $10,000 Same type of consultant. Same general skill set. 100X difference in price....
Data science bootcamps teach Python. They don’t teach you how to turn chaos into answerable questions. To this day, whenever I’m faced with a problem, the first thing I do is come up with a series of research questions - then I try to answer them. It’s standard practice in academic research, but I’ve rarely seen it done elsewhere. That experimental approach keeps me focused on what actually moves the needle. And it’s the kind of strategic thinking that gets you noticed by senior stakeholders....