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.
The Decisions ML Can't Make
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Data science exists to support decision making, but within any organisation, there is a hierarchy of decisions - low stakes, high volume decisions at the bottom; high stakes, low volume decisions at the top. Executives care most about the decisions at the top. There’s valuable work at the bottom of that hierarchy. But automating routine decisions is about clearing the path so executives can focus on the high-stakes decisions. It’s not about walking it with them. For years, data science has been synonymous with ML, but ML excels at the automation of high-volume decisions. When volume is low, it falls apart. From an executive perspective, these are the decisions that matter the least. Recently, I spoke to a property appraiser about the use of data science in his field. He made the following comment that summed this up: “You know, valuation models are now nearing the point where they might potentially be able to replace appraisers one day soon. But for the complex properties, the ones that stake up to build big risk, clients are unwilling to do that because the cost of getting this wrong is just too high.” His solution was to use data science not to replace his expertise, but to enhance it - by building statistical tools that support his job and provide him with evidence to back his recommendations. If your goal as a data scientist is to support executive decisions, you need to follow his lead. Executives want to reduce uncertainty around decisions that matter too much to get wrong, not to hand them off entirely. The path isn’t building better automated pipelines. It’s becoming the person who reduces that uncertainty. 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.
When I started my career, data science didn’t exist as a field. I trained as an actuary and statistician and those were the tools I relied on in my earliest roles. Then, around 10 years ago, I started hearing about the wonders of machine learning and became worried that my traditional training was no longer enough. So, despite already having a PhD in Statistics, I went back and completed a Masters in Machine Learning. Then came the AI wave – ChatGPT, large language models, generative AI – and...
The most valuable lessons I’ve learned in my data science career weren’t learned in a classroom. They came from conversations with people who’d already figured things out the hard way. My podcast has been a more valuable learning tool for me than all of my university degrees combined. Over 100 episodes, I’ve had the chance to speak one-on-one with some of the sharpest minds in the industry - CEOs, best-selling authors and leading researchers - on everything from cutting-edge AI to what it...
In 2015, I fell in love with a job I would never have. I’d just attended a conference where people were talking about machine learning and data science as the way of the future. I returned to the office eager to learn more and started down the data science rabbit hole - where I stumbled across an article about the recently established NYC Mayor’s Office for Data Analytics. They were using data science to locate illegal cooking oil dumping in the city’s sewers. To coordinate emergency services...