📊 “Data science is dying” is the same headline every year. And it’s always wrong.
They said it 7 months ago, 2 years ago, 3 years ago, 5 years ago. And yet people keep landing data jobs. So what’s actually going on?
The numbers don’t lie:
- Only 2.7% of Amazon’s 2022-23 layoffs were data scientists
- Data science job postings grew 130% year over year after hitting rock bottom in July 2023
- Salaries in data keep growing
The real problem: role fragmentation
The old data scientist was a Swiss Army knife: clean data, build models, present to the CEO. Today the role fragmented into 3 profiles:
- Analyst → analysis, reporting, experimentation (closer to business)
- Engineering / MLE → build and deploy ML solutions (requires 2-3 years prior experience)
- Infrastructure → pipelines, data platforms, MLOps
The problem isn’t that there are no jobs — it’s that many people are searching for the old “generic data scientist” that no longer exists.
What to do? Specialize in one of these three profiles and make your profile speak that specific language.
💡 Explanation in a nutshell#
The data market isn’t dead — it’s evolving. The “data scientist” title became ambiguous because the role fragmented. Understanding which sub-role you want to focus on (analytics, ML engineering, or infrastructure) and adapting your search is the key to navigating this market correctly.
More information at the link 👇
