ROLES &RESPONSABILITIES
Process, clean, and transform industrial data (time series).
Develop predictive and machine learning models for
Predicting failures and errors in processes and equipment.
Projecting future production and performance.
Identifying trends, anomalies, and root causes in data.
Integrating and analyzing data with tools such as Splunk.
Deploying scalable solutions in cloud environments.
technical skills:
Languages: Python (pandas, NumPy, scikit-learn, statsmodels, matplotlib, seaborn), R (optional)
Machine Learning & Deep Learning: regression, classification, clustering, anomaly detection, forecasting (ARIMA, Prophet, LSTM, neural networks with TensorFlow/PyTorch).
Databases: SQL (PostgreSQL, SQL Server, MySQL), integration with PI System and Splunk.
Environments and tools: Jupyter, RStudio, Git.
Cloud: experience with Azure, AWS, or GCP.
Solid knowledge of ETL, data cleaning, and data wrangling.