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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.

Featured Post

AI Destroys Jobs But Creates Businesses

“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:...

Machine learning excels at automating routine decisions. But the decisions that matter most are far from routine. The decisions that truly make or break an organisation are the high-stakes, one-off decisions where data is scarce, machine learning falls apart, and executive stakeholders are left relying on their gut. These are also the situations where data scientists have the potential to add the greatest value - if they know how. As a data scientist with a background in actuarial science and...

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...

Earlier this year, entrepreneur Mark Cuban posted the following on X: “There are generally two types of LLM users: those that use it to learn everything, and those that use it so they don’t have to learn anything.” As this quote suggests, AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place. Given my expertise took over 20 years for me to build, I’ve...

A few years back, when ChatGPT was in its infancy, stories relating to AI hallucination-induced mishaps made the news on pretty much a daily basis. From lawyers filing briefs referencing non-existent cases to government reports riddled with fake citations, you could watch people learning the limitations of AI in real time. And no organisation was too big to avoid embarrassment. Although these incidents do still occur, people are now at least starting to become aware of the very real...

Each week, it seems like there’s yet another announcement of technical workers losing their jobs to AI. In Australia, for example, tech giant Atlassian recently laid off 1,600 workers - 10% of their global workforce - “to steer more spending into AI”. Now, granted, not every AI-related job loss is necessarily as it first appears. Some experts point to AI-washing. That is, companies using AI as cover for restructuring decisions they would have made anyway. But regardless of the reason, the...

Have you ever seen the TV show Nip/Tuck? It centres on the lives and clients of two Miami plastic surgeons. But what it’s really about is the quest for perfection. The main characters want perfection in their own lives, but all they are actually capable of creating is the illusion of it. And beneath the perfect facades, all the characters are actually pretty terrible. It’s now over 20 years old, but rewatching an episode the other day made me realise it serves as a perfect metaphor for AI. AI...

Every organisation wants to be AI-first. It’s the new “data-driven” - a badge that signals ambition, modernity, and a seat at the table of the future. “In the long run, we’re evolving in computing from a ‘mobile-first’ to an ‘AI-first’ world.” - Sundar Pichai, Google “We are no longer a graphics card company… We are an AI-first company. From now on, we are betting the company on AI.” - Jensen Huang, NVIDIA “Duolingo is going to be AI-first.” - Luis von Ahn, Duolingo “Before asking for more...