Automated Document Categorization Using Machine Learning
Developed a machine-learning model for an automated document-categorization
system, allowing for documents to be classified into predefined categories
based on their content and characteristics. The solution included data
preparation, data analysis, feature engineering, model training and evaluation.
Causal Analysis and Statistical Modeling of Complex Large-Scale Task-Scheduling Systems
Developed a statistical model to identify causal
relationships and patterns within a task-scheduling system comprising hundreds
of thousands of interdependent, nested tasks.
Machine Learning for Detecting Malicious Website Activity and Link Injection
Analyzed web activity data, and worked on a project to detect malicious website
behavior, including link-injection attacks used for search-engine manipulation.
The model analyzed user activity to identify potential SEO spam and support
early threat detection.
Database Optimization at RTP
Analyzed and optimized the database systems supporting
satellite communications, programming schedules, and related business-critical
operational processes for RTP (Portuguese Television). Through query
optimization, indexing improvements, workload analysis, configuration tuning,
and infrastructure enhancements, I achieved an approximately 80% improvement in
overall database performance and in the systems that used it. These
improvements had a direct and measurable impact in the performance of the
systems supporting critical satellite-communication and television program
scheduling.
AWS Data Lake and High-Performance ETL Optimization for Data Science
Designed and optimized an AWS-based data lake and ETL environment to support
data science and analytics workloads. As part of the initiative, I developed
and improved ETL pipelines using AWS Glue, structured and managed data storage
in Amazon S3, and optimized querying through Amazon Athena.
By converting datasets to columnar Parquet format, implementing adequate
partitioning strategies, and selecting only the data required for each
analytical workload, I significantly reduced query execution times.
These improvements reduced typical AWS Athena query runtimes from approximately
seven minutes, down to just one second, substantially improving the efficiency
of downstream data science and analytics processes.
Multiple Large-Scale Data Analysis for Business Optimization and Performance Improvement
Conducted numerous data analysis projects across large-scale database
environments to achieve cost-reduction, performance improvements, and support
business decision-making such as identifying business oportunities.
These projects involved extracting, transforming, and analyzing data from
multiple sources; developing metrics and reports; identifying trends and
anomalies.