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.
When My "Safe" Data Science Job Stopped Feeling "Safe"
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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 science self-employment. Every independent data professional's story is unique. Take, for example, my latest podcast guest, Daniel Bourke. He became a successful independent data professional before he turned 30 - co-creating Nutrify (the "Shazam for food") and teaching thousands through Zero to Mastery Academy. But Daniel's path wasn't about dramatic risks or having it all figured out from the start. It was about strategic preparation, building momentum while still employed and learning which opportunities to say yes to (and which to turn down). In the latest episode of Value Driven Data Science, Daniel shares the practical steps that made the difference - insights that apply whether you're considering full independence, building side income or just want more control over your career trajectory. In this episode, you'll discover:
Your path doesn't have to look like Daniel's or mine - but it does have to start somewhere. Listen now on Apple Podcasts or Spotify, or click the link below: 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.
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...
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...