Data Scientist Junior
Pubblicato il 15-08-2026 - MAX MARA FASHION GROUP in Reggio Emilia
The CRM Data Scientist is responsible for analyzing large quantities of information and data (even unstructured) in order to propose models that will improve the understanding of phenomena relating to customers and the business in general (product, retail, communications, style). The CRM Data Scientist has, among its objectives, those of: modeling complex anthropological issues, identifying relevant and pertinent findings and insights through the use of statistical, algorithmic, data extraction and visualization techniques; integration and preparation of large and varied data sets, designing targeted databases, computing environments consistent with the analysis purposes and communicating the results. The CRM Scientist also manages ad hoc projects to identify the models to be used to test hypotheses and carry out data mining, as well as to establish the validity of new relationship marketing activities towards customers and determine the value, over time, of the practices launched in the different market segments. Reports directly to the CRM Director.
Qualifications
- 1.01 Designs and carries out quantitative analyzes that translate into actionable insights in terms of customer relational marketing actions
- 1.02 Knows and understands the logic of customer acquisition and engagement and the related analytical funnel
- 1.03 Develops new knowledge from the analysis of the data it uses to inspire decisions and drive business
- 1.04 Presents data and analyzes effectively to internal stakeholders
- 1.05 Capitalizes on the use of insights, findings, datasets and statistical models for various business needs
- 1.06 Deals with the pre-processing of more adequate analysis structure with respect to the objectives set
- 1.07 Uses machine learning tools and statistical techniques to produce solutions to emerging problems
- 1.08 Carry out hypothesis tests and research from which to develop analysis prototypes
- 1.09 Keeps updated on new tools and techniques of data analysis
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