Data Scientist

The goal of a data scientist is to answer business questions using data. Where a data analyst reports on what happened, a data scientist tries to predict what will happen or explain why something occurred.

Most of the daily work involves preparing data, building models, and evaluating whether those models actually work. Data rarely arrives in a clean format, so a large part of the job is cleaning and preparing it before any modelling begins. Once a model is built, you check whether it performs well on new data and present the results to people who were not involved in building it.

The main tools are Python and SQL. Common Python libraries include pandas for data preparation, scikit-learn for modelling, and matplotlib for visualisation. R is used in some organisations, especially in research or academic settings. Version control with Git is expected in most roles.

The role sits between data analysis and software engineering. At larger companies, data scientists work in dedicated teams alongside data engineers and machine learning engineers. At smaller companies, the boundaries between these roles overlap. In the Netherlands, financial institutions, insurers, and large tech employers all hire data scientists. The role connects closely to the Data Analyst and Data Engineer roles, and experienced data scientists sometimes move into machine learning engineering or quantitative research.