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.
Your Stakeholders Can't Wait 10 Years
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Around age 19, having just graduated high school, I got my first job in "data". I could barely use Excel and thought Python was something you'd find at the zoo, but a friend of the family hired me to tutor their teenaged son. My maths was good enough to help him get through high school maths. Around age 29, having just finished my PhD, I landed a role managing an insurance pricing and analytics team. I'd never heard the term "machine learning" back then, but I had spent a good chunk of the previous five years fitting GLMs. That was sufficient to build a model that reduced the number of complaints received about one aspect of the pricing process to zero. Around age 39, having just completed a Masters in Machine Learning and AI, I moved into a role as a data science technical specialist. The AI boom was still a few years off, but cloud-based AI APIs were starting to emerge. I understood the theory behind AI, but this was the first time I'd ever used AI technologies in practice. Yet, I knew enough to be able to piece together an AI data enrichment pipeline that saved hours of manual work for my team. Here's the thing... At the time I took on each of these jobs, I didn't think I was good enough. There was so much more for me to learn. Yet, I still managed to deliver meaningful results without having mastered everything. Because stakeholders are desperate for solutions NOW. They don't have 10 years to wait. Create value with what you know first. Build additional skills when that's not enough. 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...