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.
Why I Hired the "Weaker" Data Scientist (and Never Regretted It)
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I once had to choose between two candidates for a data science role. One had better credentials. The other got the job. I was hiring for a data science role within my team and we were down to the final two - let’s call them “Amy” and “Ben”. On paper, Amy was more qualified - she had better credentials than Ben. But in her final interview, all she kept doing was telling us how great this job would be for her career. She talked about the skills she’d develop, the experience she’d gain and how it would position her for future opportunities. I’m not saying that these things are wrong. In fact, there’s absolutely nothing wrong with discussing career development in an interview - it shows ambition and self-awareness. The problem was, it was only about her: How great she was. She saw herself as the customer and we were there to sell the job to her, but never explained what we would get in return. Ben, on the other hand, struck a better balance. His university grades weren’t as good as Amy’s, but there was something about his approach that suggested real drive. He’d worked his way up from customer service to data analyst, and had been systematically studying towards a professional certification. Yes, he talked about his career goals - this job would be a major leap for his career. More importantly, though, he also took the time to tell us exactly what he would do to benefit us if we hired him. He made it all about both of us: What problems he could solve for us. We hired Ben because he understood that interviews are a two-way conversation and it was one of the best hiring decisions of my life. Ben is still at that organisation and has been promoted several times since. Because Ben understood something Amy didn’t: You don’t succeed by only telling your stakeholders what you want to gain. You succeed by also showing them what you can give. 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.
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...
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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...