• Application of quantitative analysis and machine learning for optimizing pricing strategies in financial products, such as loans and banking services.• Collaborated with interdisciplinary teams across various countries in the region (Peru, Colombia, Uruguay, Central America, etc.) in financial analysis, marketing, and product development. My role involved translating quantitative analyses into pricing strategies tailored to each market.• Implemented algorithms such as K-means and DBSCAN to segment clients into homogeneous groups. These detailed customer profiles were instrumental in applying differentiated pricing and retention strategies.• Developed specialized probability models for attrition, churn, and campaigns, utilizing a variety of advanced Python models, including logistic regression, decision trees, XGBoost, and neural networks.

• Actively contributed to the successful integration of the Data Lake at Banco de Crédito, significantly supporting the seamless migration of models to various areas of the bank. This facilitated the effective adoption of these models to enhance processes and strategic decision-making.• Leveraged my expertise to generate valuable insights from Data Lake training sessions, efficiently using Power BI and presenting impactful results.• Delved into expert data extraction with PySpark, applying advanced Machine Learning techniques in Python for predictive analysis and providing valuable insights into Data Lake usage.• Gained substantial experience working with Big Data on the powerful Azure platform, expanding my knowledge and skills in large-scale data environments. This contribution has been instrumental in strategic decision-making within the organization.

• Collaborated on projects related to fraud detection in sports betting bonuses, applying models such as Logistic Regression, Decision Trees, and Clustering to identify suspicious patterns in the use of bonuses.• Contributed to data cleaning and analysis using tools such as SQL, Pandas, Polars, and NumPy, ensuring the integrity of datasets.• Worked with the Data Science team in developing algorithms to detect fraudulent behaviors specific to the use of bonuses, enhancing security in betting operations.

• Advanced data analysis to uncover strategic insights for management using tools such as Power BI and PL SQL.• Developed predictive sales models with Machine Learning in Python and created efficient tools in VBA, along with the implementation of interactive dashboards for senior management.• Utilized Python to build predictive models such as logistic regressions, decision trees, and random forests, comparing them with competitor strategies.

• Conducted a comprehensive analysis of Financial Statements (Income Statement, Balance Sheet, and Cash Flow), identifying significant trends and critical points.• Prepared detailed financial reports highlighting key indicators and relevant financial ratios to support strategic decision-making.• Collaborated directly with senior management to provide financial guidance based on in-depth analysis of financial statements.

• Developed and automated processes by creating macros in VBA, enhancing efficiency in the manipulation and analysis of financial data.• Utilized SQL to extract, manage, and analyze relevant data, enabling efficient access to databases and the execution of specific queries to assess financial risks.