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.
Tom and Adam
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As a lifelong movie fan, the two stars whose careers stand out as being the most impressive to me are Tom Cruise and Adam Sandler. Hear me out. Cruise built a career based on unrelenting excellence. The Mission Impossible movies are pretty darn impressive and that didn't happen by mistake. By focusing on making movies of undeniable quality, he was able to make his work speak louder than his personality. Sandler, on the other hand, is in many ways the anti-Cruise. Yes, Billy Madison is brilliant, but Jack and Jill leaves a lot to be desired. But that's OK. Because his brand isn't about undeniable quality. It's about his personality. Sandler makes movies that are fun to watch and enjoyable to make, with people he's worked with for years. Audiences watch them and top actors work with him because of the goodwill he's built, even if the quality varies wildly. For years, I believed that to succeed in data science you had to choose one model or the other. You could focus on building Cruise-like quality or Sandler-style goodwill, but you couldn't optimise for both. Yet, revisiting this model in the age of AI has made me realise that optimising for both is exactly what data scientists need to do. Expertise and relationships are the two areas where AI can't compete. AI can produce competent work at scale, but competent isn't excellent. So, Cruise-like excellence is now one of the few remaining differentiators - and worth pursuing now more than ever. Yet, excellence alone is no longer enough. If no one knows your work is excellent, it doesn't matter. And in a world that's now flooded with AI-generated content, standing out also requires genuine, Sandler-style human connection. For data scientists, this means knowing your stakeholders, understanding their needs and linking your work back to what they really care about. In the age of AI, the data scientists who thrive won't be the ones who choose between Cruise or Sandler. They'll be the ones who figure out how to be both. 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.
“AI destroys jobs but creates businesses.” That one statement crystallised what I’ve long believed about building a data science career. The statement was made by best-selling author and futurist Peter H. Diamandis in a recent post. Diamandis argues that while AI will lead to the destruction of white-collar jobs, as organisations replace human workers with AI, AI has made it easier than ever for entrepreneurs to launch one-person companies. The jobs that AI creates, therefore, won’t look like...
Quick quiz: If you randomly sampled just 5 people from a population of 20,000 or more, could you use that data to tell your stakeholders anything useful? For most data scientists, the answer is probably no. But according to Douglas Hubbard, author of How to Measure Anything, “you need far less data than you think.” Here’s the proof: If you randomly sample just five people from an organisation of any size and record the lowest and highest values of whatever you’re measuring, there’s a 93.75%...
I learned the true meaning of accountability on a Sunday morning, many years ago, when I had to go into the office to fix a calculation mistake made by a member of my team. I was an insurance pricing manager at the time. My team performed premium calculations that brought in $2 billion of revenue. It was complex work, spread across multiple staff, and the margin for error was incredibly low. When my boss spotted an error in a table my team had produced, it was all I could do not to say:...