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.
My PhD Didn't Prepare Me For This
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My first month in industry as a data scientist, I drafted a beautifully structured email for my boss. What he actually sent was 1/10th the length. I’ve never felt more overeducated and underprepared. Fresh from my PhD, my boss asked me to draft an email for him. I spent 4 hours crafting what I thought was the perfect message: grammatically perfect, clearly justified, with every logical step meticulously explained - exactly how I’d been trained to write at university. When he CC’d me on what he actually sent, I wanted to crawl under my desk. His version? “Hey mate, here’s the outcome we reached, cheers.” That’s when it hit me. I had spent years earning a PhD that supposedly made me “highly qualified,” yet I was completely unprepared for the reality of working in industry. My academic training had left me behind my colleagues in ways I hadn’t anticipated. More than a decade has passed since I left academia, and I’m pleased to say I survived. And over the years, I’ve come to realise my experience was far from unique. In the latest episode of Value Driven Data Science, I sit down with Dr Sayli Javadekar, who recently made the leap from a tenure-track academic position to working as a data scientist at Thoughtworks, to discuss the challenges she faced and the strategies that helped her navigate this difficult transition. You’ll discover:
This conversation won’t sugarcoat the transition from academia to industry, but it will show you how to navigate it. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 92: Making the Academia to Industry Leap in Data Science 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.
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...
Data science exists to support decision making, but within any organisation, there is a hierarchy of decisions - low stakes, high volume decisions at the bottom; high stakes, low volume decisions at the top. Executives care most about the decisions at the top. There’s valuable work at the bottom of that hierarchy. But automating routine decisions is about clearing the path so executives can focus on the high-stakes decisions. It’s not about walking it with them. For years, data science has...