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.
The Bad News (Data) Bear(er)s
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Most data scientists think the hardest part of experimentation is the statistics. It’s not. It’s telling people their ideas didn’t work. Here’s a reality check about experimentation: Even at companies like Google and Netflix, 70-90% of experiments don’t show positive results. That means if you’re running A/B tests, you’ll be delivering “bad news” far more often than good news. Now imagine being the data scientist who constantly tells people their ideas didn’t work. How long before stakeholders start avoiding you? This is why office politics becomes critical when you’re doing experimental work. You’re not just doing data analysis - you’re managing egos and expectations. Bloomberg Product Analytics Manager Miguel Curiel and I tackle this exact challenge in the latest Value Boost episode of Value Driven Data Science. In just 10 minutes, you’ll learn:
Listen now on Apple Podcasts or Spotify, or click the link below: Episode 85: The Office Politics Survival Guide for Data Science Experiments Because even perfect data won’t save you if you can’t navigate the politics. 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.
"The show doesn't go on because it's ready; it goes on because it's 11:30." - Lorne Michaels, creator of Saturday Night Live. Data scientists can learn a lot from Saturday Night Live. SNL has a rule: The show goes on at 11:30. Not when it’s perfect. Not when everyone’s happy with it. At 11:30. Many years ago, I was responsible for performing the annual workcover premium rate calculation for the whole of Victoria. It was a calculation of the utmost importance - $2b in revenue depended on it...
For the 4 1/2 years of my PhD, I worked with a de-identified dataset that felt like nothing more than numbers on a page. Cold. Abstract. Disconnected from any real human experience. Each “person” was just a line in an Excel spreadsheet, with an ID in place of a name. When I started my first role in insurance pricing, my mindset initially remained the same. That was until my boss took me along to speak to a policyholder - putting me face-to-face with one of the people my data actually...
12 years in government taught me something surprising about data science. Making money and making an impact aren’t always the same thing. The easiest way to create value as a data scientist is to help your organisation to make more money. After all, everyone wants more money, don’t they? As Elon Musk’s recent $1 trillion pay deal suggests, even the richest person on Earth. Yet, while money is valuable, money and value aren’t necessarily the same thing. And if you work for a not-for-profit or...