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.
11 Bosses in 3 1/2 Years Taught Me This Valuable Lesson
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Quick reminder: Doors close for the Data Science Impact Sprint at the end of the week. If you're interested in boosting your strategic influence and positioning yourself for career advancement, consider joining. Scroll down to learn more...
In 3 1/2 years, I had 11 managers across 2 data science roles. Let that sink in for a bit... That's an average of around 4 months per boss (and I'm not counting the short-term acting managers when people went on leave). This has got to be some sort of record. Restructures, secondments and even a retirement - my world was a revolving door of leadership. No sooner had a manager settled into a new role than one of us would move on to the next. Forget about developing my career - they were too busy figuring out their own. Here's the thing... That's OK. Because here's what I learned: you can't build a career waiting for other people to create opportunities for you. Managers come and go. Restructures happen. But your career? That's yours to manage. A few bosses in, I got tired of waiting. I started identifying problems worth solving and proposing projects of my own. I noticed IT was struggling to understand the different AI APIs across cloud platforms. So I did a comparative study - something no one had asked for, but everyone needed. This was mid-2020, during the COVID lockdowns. I had zero budget and inadequate resources. So, I set up my own trial accounts and created test data - I filmed a Rice Bubbles box to test video object recognition, tested OCR using photographs of t-shirts on my washing line and used audio from Presidential debates to test transcription. The report went "viral". I became "the AI cloud expert" and got invited to all meetings related to AI. One scrappy project I initiated myself - because I couldn't rely on my 11 managers to do it for me. While my managers were figuring out their careers, I figured out how to advance my own. If you're tired of waiting for someone else to manage your data science career, here's your chance to start managing it yourself. The Data Science Impact Sprint closes at the end of this week - my 4-week 1-on-1 coaching program that teaches you to create high-value data science opportunities that will boost your strategic influence and help position you for career advancement. Reply with the word 'SPRINT' for details. Only 3-5 spots are available for this pilot program. Doors close at 9am on Saturday 2nd August Melbourne, Australia Time (7pm Friday 1st August US EDT) or when all the places fill. 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.
When I started my career, data science didn’t exist as a field. I trained as an actuary and statistician and those were the tools I relied on in my earliest roles. Then, around 10 years ago, I started hearing about the wonders of machine learning and became worried that my traditional training was no longer enough. So, despite already having a PhD in Statistics, I went back and completed a Masters in Machine Learning. Then came the AI wave – ChatGPT, large language models, generative AI – and...
The most valuable lessons I’ve learned in my data science career weren’t learned in a classroom. They came from conversations with people who’d already figured things out the hard way. My podcast has been a more valuable learning tool for me than all of my university degrees combined. Over 100 episodes, I’ve had the chance to speak one-on-one with some of the sharpest minds in the industry - CEOs, best-selling authors and leading researchers - on everything from cutting-edge AI to what it...
In 2015, I fell in love with a job I would never have. I’d just attended a conference where people were talking about machine learning and data science as the way of the future. I returned to the office eager to learn more and started down the data science rabbit hole - where I stumbled across an article about the recently established NYC Mayor’s Office for Data Analytics. They were using data science to locate illegal cooking oil dumping in the city’s sewers. To coordinate emergency services...