Education

Florida International University, Miami FL
Bachelor of Science in Computer Engineering
- Graduated with a Cum Laude Status at FIU with a GPA of 3.56
- 8 times selected into the Dean’s list.
- Relevant Coursework: Programming 1 & 2 (Java), C & C++ Programming of Embedded Systems, Data Structures, Machine Learning, Big Data Analytics, Computer Design, Embedded System, Logic Design.

Skills

Programming Languages: Python, Java, SQL, C, C++, VHDL
Libraries: : Pandas, Numpy, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, Selenium, Flask, PySpark, Streamlit
Technologies: Git, Github, VS Code, SQL Server, MySQL, Power BI,MS Excel, Jupiter Notebooks, Google Colab, Apache Spark, Jira.

Experience

Data Scientist

Driver Boost LLC, Doral, FL – Freelance

April 2025 – Present

Tools Used: Python, Pandas, SQL, Power BI, Microsoft Excel, Scikit-learn, SQL Server

  • Clean, transform, and normalize complex multi-generational JSON pedigree data using Python, pandas, and SQL, creating a structured, analytics-ready dataset for predictive modeling and strategic decision-making.
  • Develop an interactive Power BI dashboard integrated with SQL Server, allowing the client to explore trends in performance, bloodline strength, and top-producing ancestors through dynamic visualizations and filters.
  • Constructed and deployed an interactive horse breeding recommendation system using Streamlit and Pandas, analyzing over 50,000 records to identify top stallion-mare pairings. Engineered a custom pedigree percentage algorithm and relationship labeling logic spanning four generations to support profitable, lineage-based breeding decisions.

IT Consultant / Computer Engineer

Compucot Inc, Doral, FL – Freelance

May 2025 – Present

Tools Used: Microsoft 365, SharePoint, OneDrive, Malwarebytes, Event Viewer, HWMonitor, Windows Memory Diagnostic

  • Analyzed and structured more than 800 GB of legacy data to optimize migration workflows, ensuring data integrity and accessibility during transition to Microsoft’s 365 cloud-based systems.
  • Performed comprehensive system diagnostics using data driven methodologies such as HWMonitor, Windows Memory Diagnostic, and Event Viewer to assess hardware health and performance. Identified and mitigated security risks through Malwarebytes and Microsoft Defender, ensuring system reliability, performance and protection against malware threats.
  • Designed and implemented standardized data workflows across 14 systems and 1 server, ensuring seamless integration with OneDrive and SharePoint cloud platforms to maintain data consistency, accessibility, and real-time synchronization for cross-functional analysis.

Shift Leader

Chestnut Land Auntie Anne's, Doral, FL

March 2023 – Present

Tools Used: POS System, Employee Scheduling Software

  • Oversee and maintain the Point of Sale (POS) system, troubleshooting technical issues promptly to ensure seamless transaction processes and minimize downtime.
  • Create and manage service delivery by optimizing shift coverage, employee schedules, performing excellent customer service, and enhancing team productivity while accommodating individual availability.

Projects

Here are some of the projects I’ve worked on, showcasing my skills in AI, machine learning, data analytics, and software development. From building predictive models to creating interactive dashboards and full-stack applications, each project reflects my passion for problem-solving and data-driven decision-making.

Netflix Filtering and Recomendation System

This project leverages Apache Hadoop and Apache Spark to collect, process, clean, analyze, and deploy two Netflix Movies and Shows datasets. Built using Jupyter Notebooks, Python, Pandas, Scikit-Learn, PySpark, and Tkinter, the project features a GUI with two key systems: a filtering system that returns titles based on user-selected parameters and a content recommendation system that suggests the top three most similar titles using cosine similarity.

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Concrete Comprehensive Strength Regression Model

This project leverages machine learning to predict the compressive strength of concrete based on its material composition. By applying regression models, it analyzes the impact of different components on concrete strength to enhance predictive accuracy. Built using Python, Jupyter Notebooks, Pandas, NumPy, Scikit-Learn, Matplotlib, and Seaborn, the project includes data preprocessing, exploratory analysis, model training, and performance evaluation.

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Plant Pulse Senior Project

Plant Pulse was my Computer Engineering senior desing project, developed as part of a team of five. This smart gardening system integrates IoT and machine learning to monitor plant health, using sensors to track temperature, humidity, soil nutrients, and pH levels. The data is displayed in real-time through a web-based dashboard. Built with Python, HTML, CSS, Flask, Arduino IDE, SQLAlchemy, Pandas, and Numpy. The system provides predictive insights utilizing Random Forest Classifier to enhance plant care and automate environmental adjustments.

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Ecommerce Funnel Analysis

Ecommerce Funnel Analysis is a personal data analytics project built to simulate real-world e-commerce insights, where I analyze customer behavior throughout the online purchase journey. Using SQL, I designed analytical queries and views to track user interactions from product views to cart additions and completed purchases, enabling measurement of drop-off rates and conversion patterns. The project features an interactive Power BI dashboard that visualizes funnel progression, purchase behavior by time of day, and performance across product categories, providing actionable insights to optimize the customer experience.

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Guest Intelligence Dashboard

An interactive business intelligence dashboard built to simulate the role of a Guest Intelligence Analyst. Using SQL Server, Python (Pandas), and Streamlit, I analyzed a fast-food sales dataset to extract insights on product performance, customer behavior, and time-based trends. The dashboard features KPIs, heatmaps, and visual breakdowns by item type, time of day, and transaction method, helping stakeholders make data-driven decisions to optimize sales and operations.

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Energy Consumption Prediction Model

Energy Consumption Prediction is a machine learning project developed as part of the interview process for a Data Analyst role at Accenture Federal Services where I leverage AI to optimize energy management and showcase this technology to the Deparment of Energy . The model forecasts energy usage for the east part of the country using a Random Forest Regressor over different time periods (days, months, and years) using historical data. Built with Python, Flask, Scikit-Learn, Pandas, and Matplotlib, the project includes a web-based application that allows users to input data and receive real-time energy consumption predictions.

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