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.
A data scientist friend of mine once took a role in a team that never stopped complaining about their difficulties in recruiting technical staff. She had solid data skills, but not the specific organisational knowledge the role needed. Yet, she was willing to learn, and management seemed willing to train her. So, she asked for training. And she asked again. Her boss promised to show her “as soon as an easy project came up.” The work was too important for her to make any mistakes. In the...
When the AI wave first hit, it was all about chatbots, and tech CEOs promised a future where humans and AI would work together to deliver better outcomes than humans could manage alone. Then, as AI progressively became better, businesses started to question whether they needed human workers at all, and the first wave of AI-driven redundancies hit. The tech CEOs suddenly started changing their tune and began warning of the impending white-collar job-pocalypse, especially following the advent...
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