Data Scientist & ML Engineer with real experience in churn prediction, fraud detection, RAG systems, and computer vision research — not just notebooks.
I'm a Data Science & AI undergrad at IIIT Kottayam, currently interning as a Computer Vision & Data Science Researcher at NIT Trichy. I build end-to-end systems — from raw, messy data through feature engineering, modeling, explainability, and production deployment.
My work spans applied domains including finance, customer churn, lifetime value modeling, fraud detection, and recommendation systems. I care about getting evaluation right: no leakage, honest metrics, business-grounded framing.
I compete on Kaggle and participate in hackathons to keep my edge sharp. Outside the ML stack, I'm working toward a publication on my multi-camera violence detection research — currently under peer review.
End-to-end customer intelligence platform on 541K+ transactions. ETL pipelines, feature store, churn/LTV modeling, SHAP explainability, and a real-time decision engine with A/B testing — identifying £1.89M revenue at risk.
Production-grade RAG system over live GitHub issues with hybrid retrieval (BM25 + Qdrant + RRF), cross-encoder reranking, incremental indexing, and a RAGAS-based eval pipeline. Dockerized FastAPI service with prompt-injection protection and rate limiting.
SCADA time-series pipeline for wind turbine performance monitoring and power output forecasting. Classical regression combined with LSTM-based sequential modeling for real-world industrial prediction.
Open to data science internships, ML engineering roles, and research collaborations. I respond within 24 hours.