I am a statistician with over two years of experience developing, validating, and monitoring statistical models for risk management in the financial and insurance sectors. I have worked on predictive modeling, scoring, and performance analysis in production environments, with a strong focus on turning analytical results into practical business recommendations.
I work extensively with Python, R, SQL, and data science tools such as KNIME, Earnix, Power BI, and AWS services. My experience includes exploratory data analysis, feature engineering, hypothesis testing, segmentation, and model monitoring, as well as building dashboards and automating analytical workflows.
In my most recent role as a Pricing Analyst, I supported portfolio risk evaluation across auto insurance, banking, fleet, and motorcycle products. I analyzed large datasets, tracked technical KPIs, and helped improve pricing and monitoring processes through automation and visualization.
Previously, as a Data Science Intern, I developed ETL processes, automated data analysis, implemented web scraping, created Power BI dashboards, and calibrated time series models. This experience strengthened my ability to work with structured data and deliver insights efficiently.
I have a solid academic background in statistics, complemented by a diploma in data science. My education and hands-on experience have helped me build a strong foundation in statistical analysis, predictive modeling, and data-driven decision-making.
I am comfortable working in bilingual environments, with native Spanish and professional English proficiency. I am motivated by roles where I can apply statistical and analytical expertise to solve business problems and support strategic decisions.
