Enhancing Density-Based Clustering for Time Series Data Quality Analysis

• Conduct research on data quality challenges in time series data, including missing values, outliers, structural breaks, and data inconsistencies, and investigate how unsupervised machine learning techniques can support automated detection and monitoring processes.
• Contribute to the improvement of a density-based clustering framework for time series data quality monitoring, focusing on the design and evaluation of alternative cluster formation methodologies to better identify anomalous, inconsistent, or low-quality data patterns.
• Perform a comparative analysis between the enhanced approach, the current implementation, and possibly other state-of-the-art anomaly detection techniques, assessing their effectiveness, robustness, interpretability, and suitability for large-scale financial and industrial time series datasets.

Competences: knowledge of Machine Learning and Unsupervised Learning techniques; clustering algorithms (DBSCAN, K-Means); time series analysis and anomaly detection; statistical data analysis and model evaluation; Python programming (useful libraries: pandas, numpy, scikit-learn, scipy, matplotlib, seaborn)

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Additional Information

This job description is only describing the main activities within a certain role and is not exhaustive. It does not prevent to add more tasks, projects.