Contributing to data-driven projects within financial services, focusing on the design and implementation of analytical solutions using Python, SQL and cloud-based technologies.
My work combines data processing, ETL development, and business-oriented analytics. I collaborate with cross-functional teams and stakeholders across different countries, ensuring reliable data pipelines and delivering actionable insights.
I specialize in SQL optimization and data modeling, Python-based data processing and automation, PySpark for distributed data workloads, applied Machine Learning (scikit-learn, foundational deep learning frameworks) and exposing analytical solutions via lightweight APIs.
Currently strengthening my expertise in scalable data processing and production-oriented AI systems, bridging business understanding with technical implementation.
my_stack = {
"languages": ["Python", "SQL", "JavaScript"],
"data_processing": ["pandas", "NumPy", "PySpark"],
"machine_learning": ["scikit-learn", "statsmodels", "foundations of deep learning (PyTorch / TensorFlow)"],
"data_visualization": ["matplotlib", "seaborn", "Power BI", "Tableau"],
"databases": ["PostgreSQL", "MySQL", "MongoDB"],
"data_engineering": ["ETL pipelines", "SQL optimization", "data modeling"],
"cloud": ["Azure", "AWS (foundational)"],
"other": ["Git/GitHub", "REST APIs", "Flask", "Linux & Bash"]
}-
Machine Learning Pipeline for Imbalanced Data: Stroke Prediction
This project demonstrates a complete Machine Learning workflow for stroke prediction, including data preprocessing, visualization, model training, evaluation and class balancing techniques. -
Feelms: Emotion-Based Movie Recommendations with Machine Learning and AWS
Feelms is a movie recommendation app that uses collaborative filtering and machine learning to suggest films based on emotions, integrating AWS-hosted MySQL databases and a Streamlit UI for real-time interaction. -
Financial Data Management: Building a MySQL Database for Asset Analysis
This project designs and manages a MySQL database to analyze the performance of various assets in response to economic factors, integrating SQL queries, Python automation, and data visualization for financial insights. -
Vanguard A/B Testing: Evaluating the Impact of a New UI on Process Completion Rates
This project evaluates Vanguard's new UI impact on process completion rates using A/B testing, SQL, Python, and statistical analysis, identifying efficiency gains and optimization areas. -
Want to see more?
Explore the rest of my projects and experiments on my GitHub profile.