Daniel Martinez Villegas

Daniel Martínez Villegas (- Cali, Colombia- Data Scientist / Full Stack Dev github.com/Daniel1martinez2 linkedin.com/in/danielmartinezv Data scientist and full-stack software developer with strong expertise in data analysis, machine learning, and software engineering. Proficient in Python and statistical modeling, with hands-on experience in developing scalable data pipelines, performing advanced data analysis, and building predictive models. Skilled in front-end development using React, Next.js, and TypeScript, as well as back-end technologies like Node.js, PostgreSQL, and GraphQL. Adept at leveraging modern frameworks and tools to deliver end-to-end data-driven solutions. Passionate about solving complex problems and creating impactful applications in data science and software development. SKILLS Tools and Languages Quantitative Research Communication Project Management Data Science Frameworks Front and Back-End Development Deployment and Automation Python, SQL, JavaScript, TypeScript, React, Next.js, GraphQL, Node.js, Docker, Git, Ruby on Rails, MySQL, PostgreSQL Mongo DB Statistical modeling, Machine learning (Supervised and Unsupervised), Time series analysis English (fluent), Spanish (native) Agile methodologies, Team leadership, and Task tracking tools like Jira Scikit-learn, TensorFlow, Pandas, Numpy React, TypeScript, Next.js, Flask, Supabase, Tailwind CSS, Shadcn, SCSS, Material UI Docker, AWS (Sagemaker), pipelines TECHNICAL EXPERIENCE Data Scientist and Backend Developer August 2024 — Present LiteoMed Remote, France • Developed cutting-edge oncology data-driven applications tailored for medical professionals using Ruby, Ruby on Rails, and Python for backend architecture. • Optimized machine learning prompts for precision medical insights leveraging DSPY and Claude API. • Utilized PostgreSQL for efficient data storage and querying, ensuring scalable operations. • Implemented robust Docker-based pipelines for seamless deployment and streamlined development workflows. Full Stack Developer January 2022 — November 2024 Freelance Remote, Colombia • Architected and delivered a comprehensive application for bartending academies using Node.js, TypeScript, and Next.js, enabling seamless management of students, teachers, and administrative activities. • Integrated Material UI and Tailwind to create responsive and engaging user interfaces with gamification features. • Built secure document upload functionalities using Supabase and implemented a scalable database architecture to manage academy activities. • Utilized Docker for environment consistency and deployment across multiple stages. Full Stack Developer August 2023 — August 2024 Tranqui Finanzas Remote, Colombia • Engineered ETL pipelines in Node.js to transform raw CSV bank data into normalized PostgreSQL database formats, improving fintech data processing capabilities. • Enhanced the product experience by implementing React and Material UI with a focus on UI/UX best practices. • Managed production deployments using Docker and AWS, ensuring system reliability and scalability. • Streamlined operations by designing efficient pipelines for data normalization and integration. Frontend Developer February 2022 — July 2023 Larvol Remote, United States • Built oncology-focused data visualization tools in Angular, including Kaplan-Meier estimators for analyzing drug efficacy across medical trials. • Leveraged CSS for responsive designs to enhance user engagement and data representation. • Collaborated with cross-functional teams to ensure accuracy in comparing drugs for different cancer types. • Delivered an interactive and user-friendly interface for healthcare professionals. Frontend Developer August 2021 — January 2022 RXXO Remote, United States • Created a blockchain-based project integrating wallet connectivity and secure transactions using Next.js, SCSS, and Web3.js. • Enabled seamless interactions with decentralized applications via Ethers.js, WalletConnect, and Metamask. • Applied Web3 standards and Alchemy to ensure compliance with blockchain network requirements. • Designed scalable frontend systems for reliable blockchain interactions. Daniel Martínez Villegas (- Cali, Colombia- Data Scientist / Full Stack Dev github.com/Daniel1martinez2 linkedin.com/in/danielmartinezv Full Stack Developer January 2023 — December 2023 ICESI Remote, Colombia • Built "ICESI para ti," an application for freshmen events, using React and TypeScript to create dynamic student and administrator views. • Implemented real-time updates with websockets and Firebase to synchronize data changes instantly. • Employed TanStack for advanced state management and enhanced UI functionality. • Delivered a polished application with robust React best practices and responsive design principles. Full Stack Developer June 2022 — December 2022 ICESI Remote, Colombia • Created "Code of Champions," a gamification platform for Systems Engineering students, using Next.js and Material UI for an engaging interface. • Architected and implemented the entire application lifecycle, from database structure in Firebase to deployment. • Designed scalable systems and implemented efficient workflows to support real-time interaction and data analytics. • Delivered a seamless and user-friendly platform tailored for student engagement and achievement tracking. EDUCATION Master’s Degree in Data Science, ICESI University Bachelor’s Degree in Interactive Media Design, ICESI University January 2024 — June 2025 June 2018 — June 2023 PERSONAL PROJECTS Smart Literature Review with LLM • Developed an automated system to process research papers using OCR for PDF-based information extraction. • Leveraged Python and the DSPY library to integrate local LLMs like Mistral NeMo Instruct for extracting insights from the papers. • Designed a pipeline to structure extracted insights into a JSON format, enabling visualization with tools like JSON Crack for better data exploration and presentation. • Facilitated advanced literature reviews for data-driven insights in academic research. Deep Learning Project: Penguin and Turtle Classification • Built a deep learning model with multiple output heads to classify images of penguins and turtles, and identify bounding boxes. • Utilized PyTorch and scikit-learn to design and implement the training and testing systems. • Achieved 100 percent accuracy in classification and 86 percent accuracy in regression tasks, demonstrating robust model performance. • Developed an end-to-end workflow, from dataset preprocessing to final evaluation, optimizing both classification and bounding box regression. Titanic Survivors Kaggle Challenge • • • • Designed and implemented machine learning pipelines to predict Titanic survivors using various models. Tuned and optimized an XGBoost model, achieving 98 percent accuracy on the test set. Conducted feature engineering and data preprocessing to improve model performance and accuracy. Demonstrated expertise in applying machine learning algorithms to solve real-world classification challenges.