The Evolution of Data Science
Career Reflections • Data Science
Remember when we all wanted to be data scientists (DS)? 🤔
Reflections on the many meanings of data science that I have come across over the years.
Five years ago, data science was the dream job for everyone that entered the world of Python 🐍
The pitch was irresistible: build machine learning models 🤖, create predictive analytics 📈, do all the cool technical stuff that would transform businesses. Then reality hit. 💥
For many who made it into those coveted roles, the day-to-day looked... different. Less time building ML models, more time cleaning data 🧹. Processing it. Analyzing it. Curating it. Dashboarding! Trying to make sense of information scattered across systems in a dozen different formats.
It turns out most companies' data just wasn't ready for the predictive analytics dream yet. The foundation needed to be built first. 🏗️
So what's changed?
These days, I'm noticing something interesting. We are seeing ML Engineers, Research Engineers and AI Engineers roles becoming more prominent which once were seen to be part of a DS role.
Companies are still hiring data scientists, but the role has quietly evolved. It's become more... honest, maybe? The expectations of a DS seem to better reflect what the work actually involves.
Today's data scientists often wear multiple hats 🎩 a bit of data engineering here 🔧, some analysis there 📊, and now with AI everywhere, maybe some prompt engineering or model fine-tuning too. The role has expanded to meet companies where they actually are in their data journey, not where the hype said they should be.
Maybe that's actually the story here, not that data science failed to live up to expectations, but that it matured into something more grounded and, arguably, more valuable. 🌱
I'm curious what others have experienced. Did your data science role evolve like this? Or did you find something completely different? 💬