Engineering student building analytics systems focused on financial analytics, business intelligence, dashboard architecture, and institutional-style data workflows.
Self-driven learner focused on building institutional-grade analytics capabilities across data, finance, and business intelligence.
Started with Python & SQL
Built exploratory analytics projects
Developed dashboards
Explored financial analytics
Built GitHub portfolio
Preparing for analytics internships
Moving toward advanced analytics and data science
End-to-end analytics work with real datasets, SQL pipelines, and business insights.
End-to-end e-commerce analytics on the Olist dataset using SQL and Tableau. Uncovered revenue trends, customer behavior, and regional sales performance across 95K+ orders.
Cohort analysis on Olist e-commerce data to measure customer return rates over time. Built retention heatmaps and analyzed monthly cohort behavior patterns.
RFM segmentation analysis identifying Loyal, Champions, At-Risk, and Lost customer segments. Delivered actionable insights for retention and revenue optimization.
Deterministic end-of-day reflection system using decision tree logic. Demonstrates knowledge engineering, system design, and psychology-to-data translation without LLM dependency.
Selected work remains confidential due to project restrictions. Never exposing confidential project content.
30+ SQL queries across e-commerce analytics with production-style data pipelines.
Every project connects analysis to actionable business recommendations.
Interactive Tableau dashboards deployed for stakeholder-ready insights.
Documented repositories with README insights, SQL scripts, and visualizations.
Experience with delinquency, vintage analysis, stress testing, and IFRS9 concepts.
Continuous upskilling through projects, datasets, and analytics exploration.
Structured approach to KPI definition, hypothesis testing, and insight generation.
Sustained project delivery demonstrating reliability and follow-through.
Current areas of active exploration and skill development.
Segmentation, cohort analysis, and behavioral pattern recognition.
Risk metrics, delinquency trends, and portfolio performance analysis.
Cohort retention, churn signals, and lifecycle value modeling.
Stress testing frameworks, vintage curves, and monitoring dashboards.
Institutional-grade BI architecture and interactive reporting layers.
Translating complex analytics into clear, decision-ready narratives.
Open to Data Analytics, Business Intelligence, and Financial Analytics Internship Opportunities.