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.
5 years ago, my data science “superpower” was code optimisation. I could take a piece of analysis code that took 30 minutes to run, and with a few tweaks, make it run in 2 seconds or less. Spoiler alert: the secret is vectorisation using NumPy. It was a trick I picked up while doing my Computer Science Masters, where several assignments included strict runtime constraints. In fact, I got so good at doing this, that I often found myself spending hours optimising code, even when the potential...
First of all: Time is running out to join the Data Science Impact Sprint. Doors close in 2 days. If you think this might be a good fit for you, reply with the word "SPRINT" for details. More below... When I was in high school, there were the science nerds and the arts geeks. We lived in completely separate worlds - and I was convinced mine was superior. The science nerds did maths competitions and went on to study engineering, medicine and statistics. The arts geeks did debating and went on...
How do you extract trustworthy insights from data you know has been deliberately manipulated? It's a challenge most data scientists never face. We're used to cleaning messy data, but deliberately manipulated data? That's completely next level. Yet if you're working with social media data, manipulation is the reality you're dealing with. Tim O'Hearn, a reformed social media hacker who generated millions of followers through bot manipulation, recently shared with me the harsh reality: "During...