Predictive Development Analyst and ninth-semester Systems Engineering student, focused on building solutions based on factory data, designed around the operational context and the specific needs of each strategic partner. My work is oriented toward generating operational intelligence to turn data into decisions.

I have experience implementing data ingestion and processing solutions from multiple sources (sensors, PLCs, and SCADA systems), as well as exploratory data analysis (EDA): structuring, profiling, cleaning, normalization, and assessment of information. This establishes a solid foundation for the next step: descriptive analysis.

From these processes, I develop machine learning models focused on prediction, pattern identification, and event correlation, as well as Business Intelligence dashboards (especially APMT) and custom APIs, integrating operational context and the technical judgment of the Condition-Based Maintenance (CBM) team.

Complementarily, I have experience in frontend development, building responsive interfaces and integrating APIs, which allows me to bring a more comprehensive perspective to the implementation of technological solutions.

• Turn factory data into actionable operational intelligence
• Design ingestion and processing pipelines from sensors, PLCs, and SCADA systems
• Build machine learning models for prediction and pattern detection
• Integrate operational context and technical judgment from Condition-Based Maintenance (CBM)
• Close the loop with BI dashboards and APIs that communicate value to the business

Python · InfluxDB · SQL · SPARQL · Docker

Implementation of acquisition and processing solutions for data from multiple operational sources (sensors, PLCs, SCADAs), with a focus on data quality and traceability.

• Structuring and profiling of raw plant data
• Cleaning, normalization, and quality assessment
• Reproducible pipelines in Python notebooks and scripts
• Storage in relational and time-series databases (InfluxDB)

Python · scikit-learn · Pandas · Google Colab · Databricks

Development of machine learning models applied to industry, oriented toward anticipating failures, identifying patterns, and correlating operational events.

• Exploratory (EDA) and descriptive analysis as the basis for modeling
• Forecast (time-series prediction) to anticipate the behavior of operational variables
• Classification to label states, operating modes, or failure types
• Clustering to segment behaviors and discover unsupervised groups
• PCA (Principal Component Analysis) for dimensionality reduction and exploration
• Anomaly detection on sensor data for early warnings
• Integration of technical judgment from the Condition-Based Maintenance (CBM) team
• Validation with real cases from the partner's operational context

APMT · Power BI · Grafana · REST APIs

Building BI dashboards and custom APIs that deliver analysis results to operational and decision-making teams.

• Monitoring and diagnostic dashboards (APMT, Power BI, Grafana)
• REST APIs to expose models and queries to data sources
• Visualizations designed around end-user needs

Angular · Bootstrap 5 · HTML5 · CSS3

Building responsive interfaces and integrating APIs, contributing a holistic perspective in the implementation of technological solutions.

Languages & data analysis:

Machine Learning & Deep Learning:

Databases:

Business Intelligence:

Frontend:

Tools & DevOps:

📧 analyticscbm@idc-confiabilidad.com · 💼 LinkedIn · 📍 Colombia