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.
Data Science and Zombies
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My zombie apocalypse binge taught me something about data science: Sometimes you need to break up with your favourite technique. This Friday is Halloween, which, being a life-long horror nerd, I would normally treat as an excuse to write a post connecting data science to horror movies (as I did in 2024 and 2023). However, after binge-watching way too many seasons of The Walking Dead, I started to feel like I was trapped in my own apocalyptic nightmare. The endless cycle of zombies, violence and despair started wearing me down. So I decided to break up with the horror genre. To my surprise, almost instantly I started to notice movies and TV series from other genres I hadn’t previously realised existed. And some of them are really good - better than The Walking Dead. It wasn’t that these shows had only just been made (some of them are 10+ years old), but I had become so fixated on choosing entertainment from one genre that I had become completely oblivious to the rest. I’ve met data scientists who get trapped in exactly the same way. I used to work with one data scientist whose area of expertise was generalised linear models. It was also his solution to everything. Customer churn prediction? GLM. In the entire time I knew him, I don’t think he ever proposed a single solution that didn’t involve fitting a GLM. But as the no free lunch theorem states, there is no one single model or technique to rule them all. Yet, while you’re fixated on that one approach, it can be hard to see anything else. The solution? Break up with your pet technique. Next time you reach for your go-to method, force yourself to try something completely different first. Linear regression instead of random forest. I dare you to go cold turkey for a week or two and see what alternative solutions emerge. You might discover something that makes your old favourite seem like a zombie - ready to be put out of its misery. And if not? Your favourite technique will be waiting for you when you return. After all, it’s not the end of the world. Happy Halloween and 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 was 9, I didn’t want to be a data scientist. I wanted to be a radio host. My favourite “game” involved recording episodes of my radio show using an old cassette recorder and blank audio tapes. I would bring on my family and stuffed animals for interviews, then cut to my favourite songs - sometimes sung (badly) by me. So, when I launched Value Driven Data Science three years ago, I saw it as a great way to learn from data scientists I admired, while living out my childhood dream. What I...
“So this is Christmas and what have you done?” - John Lennon It’s that time of year again. The time when work grinds to a halt and everyone tells themselves they’ll “figure it out in the new year.” Then January hits. Nothing changes. Before you know it, another year’s over and you’re scratching your head, wondering how you got so little done. It’s not that you can’t achieve big things in a year. It’s that most people never actually start. Maybe you’ve spent years building technical skills and...
If you think the best use of LLMs in data science is coding, then you’re missing some of the most powerful opportunities. Late last year, I had an important conversation coming up with a key stakeholder who I’d known for years. And when I say important, I mean the sort of conversation that could make or break a career. Let’s just say that this person had a very particular way of responding to situations, and I knew from experience that if I made one false step, the conversation could go...