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.
Last month, Anthropic CEO Dario Amodei published an essay calling for stronger AI regulation and a slower pace of AI development. Since then, two incidents have made his case for him. In the first, the US military almost started a war based on the contents of a confidently wrong AI-generated intelligence report. In the second, OpenAI disclosed that its agent hacked into Australia's Medicare website during training and accessed data that, although not sensitive, had yet to be publicly...
I once met a data scientist who was so good at his job that he automated his way out of it. He systematically built automations to handle his team’s work and once those automations were in place, the team no longer needed as many people. So, his role was ultimately made redundant. He’d built those automations because he believed they’d create business value for his employer - and they did. The problem was that the value ended up residing in the code, not in him. And because his employer owned...
The hardest course I took during my entire data science education was a theoretical computer science class. But not for the reasons you'd think. Theoretical computer science is basically pure maths for data scientists, and the material was genuinely challenging. I spent 30 hours/week studying just to keep up. Yet, the reason why this course became known as "the widow maker" wasn't just the time commitment. It was because referring to any materials beyond the prescribed texts was considered an...