Data Science jobs at European startups
Data Science and Analytics roles turn the company's data into decisions: metrics, experiments, models, and the dashboards leadership actually trusts. Part analysis, part engineering, part storytelling.
Data Science and Analytics roles turn a startup's data into decisions. That spans the everyday metrics and dashboards leadership relies on, the experiments that tell you what is working, and the models that power product features. It is part analysis, part engineering, and part telling the story clearly enough that people act on it.
At an early-stage company you often build the data function from close to nothing: the tracking, the warehouse, the first trustworthy numbers. The value is not a clever model in isolation, it is a decision that gets made because the data finally made it obvious.
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Frequently asked
What does a Data Science role at a startup involve?
A mix of analytics (metrics, dashboards, experiments), data engineering (pipelines, the warehouse, clean tracking), and modelling. At small companies one person spans all of it; larger teams split analytics, data engineering and ML into separate roles.
What is the difference between Data Science and Data Analytics here?
Analytics focuses on understanding what happened and informing decisions; data science extends into predictive models and experimentation. The line blurs at startups, where the same person often does both.
What skills do startups look for in data roles?
Comfort with SQL and a data stack, sound statistical judgement, and the ability to turn a messy question into a clear answer a non-technical founder can act on. Communication matters as much as modelling.
Do data roles require a PhD?
Usually not. Many startup data roles value practical analytical skill and business sense over an academic background. Some specialised machine-learning roles do look for deeper research experience.
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