Patrick Gomes Email & Phone Number

Rio De Janeiro, Brazil

1) Forecasting Bank Liquidity with Machine Learning:Utilized time series modeling techniques in machine learning to forecast bank liquidity, resulting in an average financial improvement of R$ 600 millions. This strategic insight allowed for optimized capital allocation and enhanced financial planning.2) Exploratory Data Analysis in Finance:Conducted exploratory data analysis on extensive financial datasets to identify critical patterns. This analysis informed strategic decisions, aiding in risk reduction and uncovering new opportunities for revenue growth. Though the direct financial impact is not quantified, the value added through informed decision-making was significant to the company's strategic direction.3) Engineering for Machine Learning Model Efficiency:Implemented advanced routines for training and storing machine learning models, significantly improving model accuracy over time and simplifying maintenance processes. This enhancement not only facilitated quicker adjustments to financial forecasting models but also ensured that the bank remained adaptive to changing market dynamics, maintaining a competitive edge.

1) Automated AI Prompt GenerationDeveloped an automated AI and LangChain system for prompt generation, integrating document analysis and web scraping. Enhanced content creation efficiency and information retrieval, improving user engagement.2) Machine Learning for Pattern DiscoveryEmployed machine learning to uncover data patterns, informing business strategies. Enhanced product offerings and customer satisfaction through targeted strategies.3) Statistical Modeling for Consumer BehaviorBuilt logit and OLS models to estimate consumer behavior variables. Insights shaped marketing strategies, increasing campaign ROI.4) Data Analysis for Business IntelligenceCreated dashboards for real-time business insights, supporting strategic decisions. Improved operational efficiency and decision accuracy.5) NLP for Content Strategy EnhancementIdentified correlations between stemmings, terms, and topics using NLP, improving content relevance and user engagement through sophisticated content strategies.6) ETL for Data ManagementDeveloped ETL routines for efficient data collection, transformation, and storage. Supported advanced analytics and data-driven decisions across the organization.

São Paulo, São Paulo, Brasil

1) Market Insights through AnalyticsUtilized statistical techniques, data visualization, and storytelling to extract actionable market insights, enhancing strategic decision-making.2) RPA for Process EfficiencyBuilt automation bots using Selenium, BeautifulSoup, and Requests, streamlining repetitive processes and improving operational efficiency.3) Advanced ETL ProcessesEmployed Airflow to automate ETL processes in MongoDB and MYSQL databases, ensuring data integrity and availability for analytics.4) Development of Metrics and ToolsCreated innovative metrics and tools to deepen data analysis and market study capabilities, driving informed strategic initiatives.5) Spatial Data Analysis for Geolocation InsightsApplied geostatistical techniques and spatial econometrics in Python for mapping and analyzing geolocated data, enriching geographic insights.6) DOOH Marketing OptimizationAnalyzed OpenStreetMap data with machine learning to enhance DOOH marketing placements, maximizing visibility and audience engagement.7) Prompt Engineering for Cost ReductionDeveloped an MVP using database and machine learning algorithms for AI prompt generation, which evolved into a product, reducing freelancer hiring costs by 90%.

Rio De Janeiro, Brasil

São Paulo, Brasil

1) Business Intelligence DevelopmentDeveloped interactive dashboards and performed database manipulation using AWS QuickSight, MySQL, and Python, enhancing real estate financing decision-making and strategic insights.2) Database Management and MigrationManaged database migration, schema creation, and mapping with MySQL and Python, ensuring data integrity and streamlined database operations for the Credit for Real Estate Financing sector.3) Data Governance and Quality AssuranceEstablished comprehensive data quality rules using Amundsen, Airflow, Docker, and Python to maintain high standards of data governance and quality, fostering trust in data-driven decisions.4) Exploratory Data Analysis for Business InsightsConducted exploratory data analysis using Python and Jupyter Notebook to uncover insights, addressing critical business questions and supporting evidence-based decision-making in real estate financing.5) Artificial Intelligence for Pattern RecognitionImplemented computer vision using Street View and Google Cloud Vision APIs for advanced pattern recognition in data, driving innovative solutions in real estate financing technology.6) Data EngineeringCreated Python scripts for automated document collection and spreadsheet manipulation through web scraping and crawlers, significantly improving operational efficiency and accuracy in data handling.