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.
Stop Selling Dashboards. Start Selling Decisions
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Imagine going to buy a house and the real estate agent just hands you a floorplan - without any photos or context. You’d see the number of bedrooms and bathrooms; the size of the house. All the important features. But would you buy it? Probably not. Because floorplans show features. People buy benefits. My parents’ neighbour recently moved house after 30+ years. For her new home, she chose a single-story house. The feature? No stairs. Real estate agents get this. But this is where many data scientists go wrong. When communicating with stakeholders, many data scientists instinctively lead with features:
But stakeholders don’t wake up wanting dashboards. They wake up wanting solutions to business problems. They’re thinking: “I need to increase revenue so I don’t have to lay anyone off” or “I need to reduce costs so we can expand.” So, stop leading with what you’ll build. Lead with what they’ll gain. Here’s what that looks like… Instead of: “Would you like me to build a churn prediction model?” Instead of: “I can create a dashboard for your sales data.” Features describe your process. Benefits describe their outcome. And nobody buys your process. They buy their outcome. What benefit are you actually selling? 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.
Data scientists love to jump straight to machine learning. New problem? Throw data at a neural network. See what happens. But there’s a foundational step that, in the right circumstances, can dramatically increase your chances of project success - and most data scientists skip right over it. Mathematical modelling from first principles. I’m talking about physics here. Mass conservation. Energy conservation. Newton’s laws of motion. The stuff you find in high school physics textbooks. In this...
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...