Data Analyst and RPA Automation Analyst

Developed a Python RPA agent to automate purchase-entry registration in the SIESA ERP system, eliminating manual data entry for the purchasing department.

Integrated PyAutoGUI and OpenPyXL to read consolidated invoice data from Excel, map product and supplier codes against internal databases, and autonomously complete ERP forms. Applied fail-fast validation logic to ensure data integrity and achieve zero data-entry errors.

Reduced manual processing time and improved operational efficiency for the purchasing team.

Conducted applied research and prototyped artificial intelligence solutions for an MVP focused on hyperrealistic digital avatars.

Developed pattern-analysis prototypes for unstructured data and improved early anomaly detection using predictive AI techniques. Validated and refined generative AI outputs for realistic interactions through advanced prompting and LLM response fine-tuning.

Supported the project data pipeline through data collection, cleaning, structuring, and analysis to prepare inputs for machine learning models.

Designed analytical workflows using generative AI and business intelligence tools to capture and process unstructured information from reports and inspections.

Used advanced LLM prompting to extract preventive insights from large volumes of textual data and accelerate the identification of relevant risk patterns. Applied K-Means clustering and semantic embeddings to segment risk categories and generate differentiated recommendations.

Performed STL decomposition on incident time series to identify seasonality, long-term trends, and residual anomalies that supported strategic decision-making.

Managed and analyzed field data, organizing collected information into relational databases for later querying and visualization.

Supported the preparation of monitoring reports and performance indicators using data analysis and visualization tools.

Designed and implemented an integrated management system from the ground up, structuring processes for operational data capture, registration, and traceability.

Created documented procedures and data matrices for high-impact activities, helping standardize information management. Managed performance indicators and monitoring reports to support decisions based on historical data and operational trends.

Developed an end-to-end analytics solution integrating computer vision, time-series analysis, and automated clustering for occupational risk analysis.

Built a data pipeline combining object detection, STL temporal analysis, and K-Means segmentation of sectors. Created Power BI dashboards with DAX metrics and MySQL connectivity for strategic real-time visualization.

Automated ingestion and transformation of unstructured text and image data into relational tables, reducing manual analysis time.

Professional in Occupational Safety and Health. Resolution 3702 of 2023 and Resolution 615 of 2014.