I build data-driven and machine learning solutions with a focus on clean pipelines, reproducibility, and real-world impact. My work sits at the intersection of data science, data engineering, and product decision-making, using Python, SQL, and modern analytics tooling.
I bring a strong mix of machine learning, analytics, and cloud engineering, and I am particularly interested in applying data science to financial products, user behavior, risk, and decision systems at scale.
- Build and evaluate machine learning models for prediction, optimization, and decision support
- Design clean, reproducible data pipelines using Python and SQL
- Analyze complex datasets and translate findings into clear, actionable insights
- Work comfortably across data, engineering, and stakeholder contexts
- Leverage automation, tooling, and AI to work efficiently and responsibly
Data Science & ML: Python, Pandas, NumPy, scikit-learn, PyTorch
Data & Analytics: SQL, Excel, EDA, Statistical Analysis
Visualization: Power BI, Tableau, Oracle Analytics Cloud
Cloud & Engineering: AWS, Docker, GitHub Actions, OCI
Collaboration: GitHub, Jira, Agile workflows