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.
Do What You're NOT Told (But Someone Should Have Asked)
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Before any important meeting, I put myself in my stakeholders’ shoes and ask:
Then I have it ready when they ask. I started doing this in my graduate days, as a way to avoid staying back late for last-minute requests. But the career benefits soon became clear: anticipating what stakeholders need - before they ask - creates far greater impact than simply doing what you’re told. Which brings me to a question I was recently asked on LinkedIn: “What do you do if you want to create opportunities through your work, but management isn’t open to innovation and discourages exploratory analysis?” It’s a fair concern. Bad bosses exist. Some organisations actively discourage initiative. But here’s what I’ve learned: even the worst managers rarely discourage staff from making their job easier. The problem isn’t that you’re taking initiative. It’s what kind of initiative you’re taking. Exploratory analysis for its own sake? That’s risky. Your manager sees it as wasted time on work that might not matter. But analysis that solves a problem they’re already worried about? That’s gold. Here’s the difference: “I spent two days exploring our customer data and found some interesting patterns” = initiative without direction “I noticed you’ve been asking about customer retention a lot lately, so I pulled together some analysis that might help” = doing what should have been asked One makes you look like you’re going rogue. The other makes you look indispensable. The key to creating valuable opportunities isn’t doing analysis for its own sake. It’s understanding what your stakeholders actually need and creating work that aligns with those needs - even if they haven’t explicitly asked yet. That’s what my pre-meeting preparation habit taught me: value isn’t created by being clever with data. It’s created by being useful to the people who depend on that data. So, try this before your next meeting: Ask yourself:
Then show up with answers to questions they should have asked. That’s not just good preparation. That’s how you do the work you’re not told to do - but someone should have probably asked for. 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.
Data science bootcamps teach Python. They don’t teach you how to turn chaos into answerable questions. To this day, whenever I’m faced with a problem, the first thing I do is come up with a series of research questions - then I try to answer them. It’s standard practice in academic research, but I’ve rarely seen it done elsewhere. That experimental approach keeps me focused on what actually moves the needle. And it’s the kind of strategic thinking that gets you noticed by senior stakeholders....
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