I am a results-driven Data Scientist and Data Engineer with over 2 years of professional experience building end-to-end machine learning pipelines, ETL workflows, and AI-powered applications. I am proficient in Python, SQL, and cloud platforms such as Google Cloud Platform (GCP) and AWS. Throughout my career, I have demonstrated the ability to reduce operational costs, automate manual processes, and deploy scalable ML models into production environments.
I excel at translating complex data insights into measurable business outcomes and have strong communication skills, which have enabled me to work effectively in remote, cross-functional teams. My experience spans designing and optimizing automated data pipelines, integrating geospatial data and APIs, and supporting the deployment of machine learning models to improve decision accuracy.
I have contributed to projects involving fraud detection, fleet optimization, recommendation systems, and traffic accident analytics, applying advanced machine learning techniques and data visualization tools. I am passionate about leveraging data to solve real-world problems and continuously improving data quality and workflow automation.
My technical expertise includes programming in Python and SQL, machine learning algorithms such as Random Forest and XGBoost, and cloud deployment using GCP and AWS. I am also skilled in building REST APIs and interactive dashboards using tools like FastAPI, Streamlit, and Power BI.
I am a native Spanish speaker with professional working proficiency in English, and I am committed to ongoing learning and professional development through certifications and hands-on projects. I am eager to contribute my skills and experience to innovative data science and engineering roles that drive business value and technological advancement.
Evaluated and validated outputs generated by large language models (LLMs), ensuring high-quality responses. Detected inconsistencies, biases, and factual errors in AI-generated content. Contributed to improving data quality for model training and evaluation pipelines. Worked under strict human review and quality assurance standards in AI evaluation workflows.
Designed and optimized automated data pipelines for geospatial and financial risk analysis, processing large-scale datasets using Python and GCP. Integrated satellite imagery and third-party mapping APIs into credit evaluation ML systems, enabling real-time geospatial risk scoring. Supported end-to-end deployment of machine learning models on Google Cloud Platform (GCP), improving credit decision accuracy by ~20%. Automated data validation, preprocessing, and quality control workflows using Python, reducing manual intervention time significantly. Collaborated with cross-functional remote teams to deliver scalable ETL pipelines aligned with business and compliance requirements.
Designed and implemented ETL and reporting automation pipelines using Python and SQL, eliminating repetitive manual workflows. Developed interactive dashboards and predictive reports in Power BI used daily by management teams for strategic decision-making. Reduced manual reporting time by ~30% through end-to-end process automation and scheduled data refresh pipelines. Analyzed large structured datasets to identify business trends, KPIs, and actionable insights for commercial and financial teams. Maintained and documented data workflows, ensuring data quality, consistency, and traceability across reporting systems.
Performed detailed financial analysis and automated reconciliation processes, improving accuracy and reducing processing errors. Supported data-driven financial decisions using accounting systems and structured reporting tools. Improved cash flow monitoring and regulatory compliance processes through systematic data review.
Data Science Bootcamp
800+ hours, Remote, Buenos Aires, Argentina
Bachelor of Public Accounting
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