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# Data Scientist

- **Company:** Vivid Resourcing
- **Location:** Mechelen, Belgium
- **Experience:** Experienced
- **Salary:** €72,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Business Analytics Applications, Data Analysis, Microsoft Azure, Big Data, Cluster Analysis, Python (Programming Language), Machine Learning, Power BI, Tensorflow, Standard Sql, Tableau (Software), Unstructured Data, Feature Engineering, Pytorch, Model Validation, Scikit Learn, Xgboost, Performance Monitor, Plotly
- **Published:** August 2, 2026
- **Apply:** https://www.adzuna.be/details/5800810912

## About the Role

* 2-5 years of experience in Data Science, Machine Learning, Applied Statistics, or a related field.
 * Strong Python and SQL skills.
 * Hands-on experience developing predictive models using libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
 * Experience working with large structured and unstructured datasets and performing statistical analysis.
 * Knowledge of feature engineering, experimentation, forecasting, clustering, and model evaluation techniques.
 * Experience with cloud platforms such as Azure, AWS, or GCP is an advantage.
 * Familiarity with data visualisation tools such as Power BI, Tableau, or Plotly is beneficial.
 * Experience within Sports Analytics, HealthTech, wearable technology, performance analytics, or fan engagement platforms is considered a strong plus.
 * Excellent communication skills with the ability to present insights to both technical and business stakeholders.

## Description

As a Data Scientist, you'll join a forward-thinking Sports Analytics company leveraging data and AI to redefine how teams analyse performance, and develop strategies. You'll build advanced predictive models and analytics solutions that turn complex sports data into actionable insights, enabling smarter decisions across performance, scouting, and business operations.

Responsibilities

 * Develop and deploy machine learning models for player performance analysis, injury prediction, match outcome forecasting, fan engagement, and customer analytics.
 * Analyse large, complex datasets including player tracking, wearable sensor, event, video-derived, and historical performance data to generate actionable insights.
 * Design predictive models and statistical analyses to optimise player recruitment, tactical decision-making, and performance monitoring.
 * Build interactive dashboards and visualisations to communicate insights to technical and non-technical stakeholders.
 * Evaluate model performance and continuously improve algorithms through experimentation and feature engineering.


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