Senior Data Scientist

Headquartered in Switzerland, MDPI is a fully Open Access publisher with a portfolio of more than 500 journals across all scientific disciplines. To date, MDPI has published the works of over 4.5 million researchers, collaborating with an extensive network of academic institutions and scientific societies worldwide. Above all, MDPI is committed to ensuring that high-quality research is freely accessible to readers across the globe.

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or another STEM-related field.
• Strong background in data science, statistics, and analytical problem-solving.
• Advanced programming skills in Python and SQL.
• Proven experience developing, evaluating, and deploying machine learning models.
• Advanced proficiency in data visualisation tools and techniques to communicate insights effectively.
• Experience with version control systems (Git) and collaborative development workflows.
• Strong working knowledge of Microsoft O365 tools.
• Excellent written and spoken English.
• Analytical thinking and problem-solving.
• Effective collaboration within multidisciplinary teams.

Nice to have:

• Certification in Python or SQL.
• Coursework or certifications in Machine Learning or Data Analytics (e.g., Coursera, edX, IBM, Google or similar programmes).

• Design, develop, and validate statistical models and machine learning solutions to support editorial performance analysis, trend detection, and strategic decision-making.
• Build, deploy, and maintain machine learning models and pipelines, ensuring robustness, scalability, and reliable performance in production environments.
• Lead data preprocessing, feature engineering, and dataset validation activities using data from internal editorial systems and external sources.
• Contribute to the development and refinement of ontology structures and automated paper-tagging systems, including model evaluation and continuous improvement.
• Develop analytical tools and predictive models to identify emerging research topics and publication dynamics across journals.
• Collaborate with Data Engineering to integrate models into the data intelligence platform, supporting APIs, monitoring, and model lifecycle management.
• Ensure clear documentation of data pipelines, models, and analytical workflows to promote reproducibility and maintainability.
• Contribute to monitoring data quality, model performance, and system reliability, including alerting and anomaly detection.
• Work closely with editorial, analytics, and technical teams to translate business needs into effective solutions.