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.
Nothing You Know About Data Science is Obsolete
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I once worked in an organisation where every member of the data science team was assigned an $8,000 laptop. It sounds way cooler than it was. We’re not talking about the Rolls-Royce of MacBooks here. Rather, it was a massive brick of a device that ran Ubuntu Linux and was so heavy it rarely left my desk. My team had convinced our employer to buy them because fitting the machine learning models we needed required powerful GPUs - and with security concerns limiting our cloud access, this was considered the only way. At that time, compute power was the bottleneck to what our team could deliver, and over time, the relationship between compute and model capacity became a fundamental truth to me, as reliable as gravity. Reading about AI agents recently, however, I found myself suddenly questioning this truth. When dealing with agents, it turns out, compute is no longer the bottleneck. Context is. The rules of the game had suddenly changed. Data scientists are better placed than most to thrive in the agentic AI era. But to do so, we need to learn the new rules - and unlearn some of the old ones. In the latest episode of Value Driven Data Science, Jia Huang, lead research engineer at A*STAR and author of Designing AI Agents, joins me to explore what that rewiring actually looks like, and why data scientists are better placed than almost any other profession to make it. You’ll discover:
Nothing you know about data science is obsolete. Everything you know is one layer more valuable. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 119: Rewiring Your Data Science Thinking for the Agentic AI Era 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.
Shortly after starting work in a new role, I was assigned responsibility for a data report that my predecessor, “Charlie”, had previously maintained. The report was scheduled to run on the first day of each month and had been used for years, seemingly without a hitch. However, on examining the code behind the report to figure out how it worked, I was shocked to discover a small calculation error that meant every report run to that point had actually been wrong. Not obviously wrong. The...
Correlation does not equal causation. Every data scientist has had that drummed into them. In fact, when I taught data science, I used to be the one doing the drumming. However, as every data nerd’s favourite comic, “xkcd”, once pointed out: correlation “does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’”. This is the part most data scientists miss. Without correlation, there can be no causation and often, it’s because researchers noted the existence...
Although Python is my usual weapon of choice, I'm not averse to building the occasional model in Excel when the use case fits. Having originally trained as an actuary, there was a point in my life when I literally lived and breathed Excel. It still feels like second nature - which is probably why I've never felt tempted to use AI assistance when building Excel models, despite using it when writing code. However, after speaking recently to a fellow data professional about how much AI struggles...