Yoe is a Python Developer and Data Scientist with extensive experience working on Machine Learning/Python projects and transferring real-world problems into requirements and solution planning. With a solid background and practical knowledge in ML/AI, research, mathematics, and statistical analysis, he delivers solutions and helps businesses to achieve more.
Delivering data warehouse and ETL solutions as part of an Agile team using advanced ML techniques to improve performance and processes.
Helping build and improve infrastructure, application, and performance development and ensuring tight security including data encryption, security groups, and environment scanning.
Ensuring high-quality deliverables and implementing DevOps and security best practices in fast-paced environments.
Building pipelines with transformation/aggregation phases using PySpark and working with Databricks for collaborative analytics.
Worked on some pragmatic prevention guidelines regarding SARS-CoV-2 and COVID-19 in Latin-America inspired by Mixed Machine Learning Techniques and Artificial Mathematical Intelligence.
Used ML tools and Python to set up a sentiment analysis classifier of tweets with the TensorFlow module.
Used ML tools and Python to set up a Long-Short-Term Memory Neural Network with the TensorFlow module to forecast the Colombian coffee price.
Published numerous research papers including statistical mechanics in the portfolio optimization with Kusuoka’s representation and conceptual computation in artificial mathematical intelligence as a paradigm-shifting technique in physics and mathematics.
Reviewed methods and teaching materials and gave recommendations for improvement.
Worked on research, fieldwork, investigations, and writing up reports.
Led a 40-person team on the SFT Advanced Reasoning project, designing architectures for handling complex training datasets and fine-tuning Language Models.
Created a Flask-based app using OpenAI APIs to test various model prompts, and monitored trainer performance using ETL processes with BigQuery.
Worked with tools like AWS Lambda, API Gateway, and EC2 in previous roles to automate data collection/labeling pipelines, and studied Docker configurations for containerized services.
Utilized Machine Learning tools and Python to establish a Long-Short-Term Memory Neural Network using TensorFlow for Colombian coffee price prediction
Performed LSTM time-series forecasting and classification tasks with TensorFlow and Keras
Experimented with MLFlow for model tracking in a BERT-based Q&A system, logging metrics and hyperparameters during A/B testing for different fine-tuning strategies
