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.
No Theme Song Required
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Back in the day, while doing my PhD, I listened to podcasts pretty much nonstop. It helped break the monotony caused by hours of data analysis. Over time, the hosts of those podcasts took on god-like status in my mind. One that particularly stood out to me was Pilar Alessandra’s “On the Page”, a podcast that began with a theme song so catchy I still find myself humming it to this day. I wanted to be just like her, but never thought it possible. For starters, I didn’t even know who to turn to for a song. Fast forward to 4 years ago, when I had just started my business. I was looking for ways to boost my professional visibility. And lo and behold, I came across an ad for Jonathan Stark’s “5-day Podcast Challenge”. It promised to teach you how to launch a podcast in under a week - no fancy theme song required. I decided to give it a go. It turned out to be one of the best purchases of my life. I’ve since gone on to record over 100 podcast episodes. And there’s still not a theme song in sight. Jonathan recently invited me to join him on his own podcast, “Ditching Hourly”, to share what I learned along the way. Here are some highlights from our conversation:
You don’t need a theme song to make yourself known. Listen now at the link below: Podcasting for Expertise-Based Businesses 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.
Ten years ago, everyone wanted to be doing machine learning. Go to a data conference - someone was presenting on ML. Advertising a data role - just mention ML and applications would 10X. It was the promise of machine learning that inspired an entire generation of data scientists. I know. I was one of them. And then came the statistic that revealed what many data scientists knew but few wanted to admit: around 90% of ML initiatives never made it to deployment. The ideas were there. The...
Last week, I stepped on the fancy scales at my gym for the first time in four months. You know the ones. They don’t just tell you your weight. They tell you your muscle mass, bone density, basal metabolic rate… Basically, everything short of your star sign. The results were horrifying. Everything had gone backwards. I’d even managed to lose 400 grams of muscle. I’d been training five days a week and made no major changes to my diet. I was lifting heavier weights than ever before. None of it...
AI can get anyone to 60% of a finished output in minutes. A novice with the right prompt can produce something that looks polished, credible and complete. And to anyone who doesn’t know what “finished” actually looks like, it might even pass for the real thing. But getting from 60% to 100% - the part where real insight lives - is a different problem entirely. In a recent Forbes article, Brent Dykes mapped out exactly what happens in that gap with his Four Zones of AI Productivity framework....