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.
How to Guarantee You Win Even When Your Data Project Fails
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I've built data science solutions that nobody wanted to use. Technically brilliant models. Clean code. Perfect documentation. And exactly zero end-users. If you're a traditional employee, working within an organisation, this situation is depressing. If you're an independent data professional, this situation can be financially devastating. Yet some data professionals seem to avoid this trap entirely. Take Daniel Bourke. His machine learning courses have over 250,000 students. His Nutrify app continues to grow. He's landed consulting contracts from companies who specifically sought him out. How does he validate ideas before investing months of development time? His approach isn't about extensive market research or complex validation frameworks. It's about creating "win-win scenarios" - projects where you literally cannot lose. In the latest Value Boost episode of Value Driven Data Science, Daniel returns to reveal the practical strategies behind this approach. In this 13-minute episode, you'll discover:
The biggest risk isn't building something that fails. It's spending months building something nobody wants while learning nothing transferable in the process. 🎧 Listen now on Apple Podcasts or Spotify, or click the link below: Talk again soon, Dr Genevieve Hayes. p.s. I'm taking a break next week, but will return the following week. |
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...