Career path
Data Scientist
A data scientist uses statistics, modelling, and code to answer questions that go beyond "what happened" — toward "why", "what's likely next", and "what should we do". It's a step deeper into the quantitative end of data work, and a common move up from analytics or research.
What the job actually is
You frame a fuzzy business problem as something measurable, then use data and models to make progress on it. That spans exploratory analysis, designing and reading experiments, and sometimes building predictive models. The most underrated part is the unglamorous front end: cleaning data and questioning whether it can even support the conclusion you're after.
Skills that matter
- Statistics — the core that separates a data scientist from a chart-maker.
- Python or R — plus the data libraries that go with them.
- SQL — you still have to get the data first.
- Experiment design — running and interpreting A/B tests honestly.
- Communication — translating a model's output into a decision.
How to switch in
Many data scientists started as data analysts and deepened their statistics and coding. Others arrive from research or quantitative fields where they already model and reason with data. The move is to strengthen statistics and programming, build a portfolio of end-to-end analyses, and resist the pull toward fancy models when a simple, well-explained one would do.
Frequently asked questions
Data scientist vs machine learning engineer — what's the difference?
A data scientist focuses on understanding problems with statistics and modelling, often to inform a decision. A machine learning engineer focuses on building and running models in production software. The two overlap, and some people move from one to the other.