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.
3 Books Every Data Scientist Should Read This Christmas (None of Them Are About Python)
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After spending most of the year in front of a screen, at Christmas time there’s nothing I enjoy more than finally switching off my computer and relaxing with a good book. With only 10 days left until Christmas, here are my top picks for this year’s holiday break: Stakeholder Whispering by Bill ShanderData scientists are constantly told of the importance of understanding their stakeholders’ needs. Unfortunately, this advice is rarely accompanied by instructions on just how to do this. In Stakeholder Whispering, Bill Shander fixes this gap. Stakeholder Whispering is a no-nonsense guide to uncovering what your stakeholders really want (not just what they ask for), interspersed with stories from Shander’s 30+ year career. Shander is an excellent storyteller, so the 222 pages seem to fly. Not convinced yet? Check out my interview with Shander for Value Driven Data Science. Hidden Potential by Adam GrantOne thing I love about data science is that, as a new profession, there’s more room for talented individuals to break through than in many more established fields. Success in data science rewards the right character traits, not just credentials and connections. In Hidden Potential, social scientist Adam Grant explores the science of achieving great things - both in your own life and in supporting others to achieve - and discovers what those character traits might be. It’s an eye-opening read that may change what you believe to be possible for you in 2026. Cain’s Jawbone by Torquemada (Edward Powys Mathers) Written in 1934 by the inventor of the cryptic crossword, Cain’s Jawbone is a murder mystery with a twist: the 100 pages are printed out of order. Your job is to solve the six murders by arranging the pages in the correct (unique) sequence. Back before the internet, supposedly only three readers ever solved it - it’s marketed as the “most fiendishly difficult literary puzzle ever written”. And even with Google’s help, the puzzle is still challenging. It took me 2 months to complete. But given data scientists’ affinity for puzzles, I can’t imagine a better way to spend your Christmas break. What books do you recommend for data scientists in need of a break? Hit reply and let me know. Talk again soon, Dr Genevieve Hayes p.s. I'm opening spots in my Strategic Expert Mentorship program starting in February 2026. This isn't a technical skills course. It's 1-on-1 mentorship for data professionals who want to make the move from technical executor to strategic expert. Between now and Christmas, I'm making time to talk with people who want to know more. Interested? 👉 Book Your Call Now |
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.
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...
You’re already using optimisation every day as a data scientist - but you probably don’t think about it. Every time you train a machine learning model, you’re running an optimisation algorithm to find the best parameters. But there’s a second type of optimisation that most data scientists never even touch - and it’s where the real business value often lives. It’s called decision optimisation, and it can transform your ML predictions into actionable decisions. Here’s the difference: ML...
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...