I build forecasting models, statistical pipelines and ML systems, then turn them into usable APIs and production data services.
IIT Madras · B.S. Data Science
My projects sit between data science and backend engineering. The model matters, but so do validation, APIs, data quality, reproducibility and the path from prediction to action.
XGBoost, scikit-learn and statistical forecasting.
FastAPI, Python, SQL and Docker.
Render, Streamlit Cloud and CI/CD workflows.
Backtesting, QA checks and business impact.
End-to-end day-ahead electricity price forecasting using real ENTSO-E market data. The pipeline engineers 12+ features, compares multiple models and includes backtesting and trading simulation.
A FastAPI-based decision intelligence platform combining Logistic Regression with a provider-agnostic LLM layer for prediction, explanation and retention strategies. Includes batch processing and what-if simulation.
A multi-source pipeline combining OpenWeatherMap, TomTom Traffic and WAQI Air Quality data with validation, caching, forecasting, anomaly detection and automated alerts.
ML-based anomaly detection for cybersecurity telemetry using feature engineering and Isolation Forest.
Brand-influencer matching platform with Vue 3, Flask, Celery and similarity-based matching.
Flutter mobile app with Flask API, Groq-powered chatbot, Maps integration and medication reminders.
Power BI dashboard covering team performance, player statistics, trends and drill-through analysis.
LLM-powered desktop automation agent using Node.js and Playwright with a human approval workflow.
Python-based analysis of sales, profitability and retention with statistical modeling and business insights.
I'm looking for Data Scientist, Backend Engineer and Applied ML Engineer opportunities where I can build models and ship them as real systems.