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.
“Analyse this dataset and come back with any insights you find.” This was one of the first requests I ever received as a data scientist, and to be perfectly honest, I didn’t quite understand what it meant. Sure, I understood the “analyse the dataset” part, but at the time, I had no idea what my stakeholder meant when she spoke of “insights”. I interpreted the word as a synonym for “interesting facts”. And that’s exactly how she politely responded when I returned with my findings. “That’s...
“I haven’t written code by hand in months - and honestly, I don’t want to anymore.” This admission came from one of the most capable data scientists I know. Until recently, he was shipping enterprise-scale code at a top multinational company - without writing a line of it himself. He now builds cutting-edge AI tools for small businesses. For him, understanding the architecture, logic and business context was enough. His ability to hand-code was slowly atrophying, but something new was growing...
The first thing I ever published that attracted any real attention was just after I finished my PhD. I started writing the puzzle page for Actuaries Magazine as a way of filling my suddenly empty weekends. I didn’t expect it to lead to anything. Yet, in the years that followed, people would walk up to me at conferences and want to shake my hand because I was “the puzzle girl”. Once someone even offered me a job because of it. Prof Rob Hyndman, one of the world’s leading applied statisticians,...