I am a data scientist and chemical engineer with dual master’s degrees in Bioengineering and Artificial Intelligence, specializing in advanced data analysis, machine learning, and data-driven digital transformation. My experience spans the pharmaceutical sector, life sciences, and regulated environments, where I work with large volumes of data from critical systems, industrial processes, and digital platforms, ensuring data integrity, traceability, and regulatory compliance.
My focus areas include statistical modeling and machine learning, predictive analysis and process optimization, automation of reports and dashboards, data integrity and governance, and digital transformation based on analytics and AI. I have demonstrated the ability to convert complex technical data into strategic business insights, improve operational efficiency, and develop analytical solutions applied to quality, engineering, production, and computerized systems.
I am passionate about roles such as Data Scientist, Senior Data Analyst, Machine Learning Engineer, AI Specialist, Business/Data Intelligence Analyst, Clinical Data Scientist, Data & Analytics Consultant, AI & Digital Transformation Specialist, and Data Scientist in Pharma, Healthcare, and Life Sciences sectors.
In my recent role as Senior Project Engineer at Cercal Group, I analyzed and structured large volumes of technical and quality data from critical systems like HVAC, utilities, cleanrooms, and pharmaceutical processes. I developed statistical analyses and trend evaluations to optimize processes and ensure regulatory compliance, implemented data analytics methodologies for KPI monitoring, deviations, and operational performance, and designed dashboards and analytical reports to support strategic decision-making.
Previously, as a CSV Engineer, I analyzed data quality, integrity, and traceability in LIMS, SAP, WMS, and production systems, implementing risk analyses and functional system evaluations, validating databases and automated systems, and strengthening data governance in regulated environments. Additionally, as a Clinical Data Scientist researcher at Pontificia Universidad Javeriana, I collected and cleaned data from chemoinformatics databases, developed machine learning models using Python, R, SQL, and C++, and collaborated with engineers and data scientists to create effective drug analysis solutions.
Analyze and structure large volumes of technical and quality data from critical systems (HVAC, utilities, cleanrooms, pharmaceutical processes). Develop statistical analyses and trend evaluations for process optimization and regulatory compliance. Implement data analytics methodologies for KPI monitoring, deviations, and operational performance. Design dashboards and analytical reports to support strategic decision-making. Integrate data from multiple sources for risk analysis, planning, and resource optimization. Ensure data integrity, traceability, and reliability under regulatory standards. Apply advanced analytics for continuous improvement and digital transformation in engineering and quality projects. Key achievement: Transform complex technical data into strategic insights for operational optimization and regulatory compliance.
Analyze quality, integrity, and traceability of data in LIMS, SAP, WMS, and production systems. Structure and analyze quality, production, and laboratory data for decision-making. Implement risk analyses based on data and functional system evaluation. Validate databases, spreadsheets, and automated systems through structured analysis. Analyze functional test results and trends to ensure system reliability. Design traceability matrices and analytical reports for regulatory audits. Strengthen governance and data integrity in regulated environments. Focus on data quality, data governance, and analytics in critical systems. Analyze and ensure integrity, quality, and traceability of data in critical computerized systems under ALCOA+ principles and GxP/GMP standards.
Collect and clean data extracted from chemoinformatics databases (PubChem, ChEMBL, ZINC, ChemSpider, Enamine, among others). Analyze large datasets to identify patterns and trends to improve AI systems. Implement machine learning systems using Python, R, SQL, and C++. Design, visualize, and analyze predictive AI models. Collaborate with engineers and data scientists to develop effective solutions in multicomponent drug analysis. Conduct tests and evaluations of AI systems to ensure model effectiveness.
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