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 Irony of Scalability
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Data science is obsessed with scalability. Can your model handle bigger data received at a higher velocity than ever before? So, it's ironic that some of the best things you can do for your data science career don't actually scale. Genuinely understanding the problems of a single stakeholder - doesn't scale. Building a reputation as an expert in your domain - doesn't scale. Delivering recommendations that actually get acted on - doesn't scale. AI has made it easier than ever to produce technical work at scale. This means the things that can't be scaled are worth more than ever before. 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...
Shortly after starting work in a new role, I was assigned responsibility for a data report that my predecessor, “Charlie”, had previously maintained. The report was scheduled to run on the first day of each month and had been used for years, seemingly without a hitch. However, on examining the code behind the report to figure out how it worked, I was shocked to discover a small calculation error that meant every report run to that point had actually been wrong. Not obviously wrong. The...