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.
Keep Kicking Goals (and Other Terrible Data Science Advice)
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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 projects fail or why their data science teams seem to spin their wheels. Here’s the thing… This isn’t about one manager’s communication style. It’s really about a much deeper issue - many stakeholders simply don’t know how to direct data science efforts effectively. This is something you can’t control - what you can control is your response. Don’t wait for your boss to create opportunities for you. Create them for yourself. When you see your marketing team manually segmenting customers every month, that’s an opportunity. Instead of waiting for the next assignment, come to your stakeholders with solutions. Frame it in business terms. Show the impact. Make it impossible to ignore. “Keep kicking goals” isn’t strategy - it’s noise. But master this approach and you’ll become the player everyone wants on their team. 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...
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...
There are three ways to fail as a data scientist. Only one is about technical skills. Solving the wrong problems; Producing the wrong results; and Not being recognised for the value your solutions create. 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...