I'm a self-taught Data Scientist based in Navi Mumbai, focused on building ML systems where predictions are reliable, explainable, and directly useful for decisions — not just accurate on a leaderboard.

My work centres around a consistent theme: probabilistic modeling, uncertainty quantification, and calibrated predictions that translate into real business logic.

• Probabilistic modeling — calibrated probabilities over hard classifications
• Uncertainty quantification — prediction intervals, confidence estimation
• Explainability — SHAP-based model transparency for regulated domains
• Time-series forecasting — demand forecasting with feature engineering
• Simulation — Monte Carlo methods for season-level uncertainty

End-to-end credit risk pipeline predicting loan default probability on the Home Credit dataset.

• Platt Scaling calibration reducing ECE from 0.041 → 0.004
• Risk bucketing (Low / Medium / High / Very High) aligned with lending policy
• SHAP explainability for individual applicant decisions and regulatory transparency
• Python Scikit-learn XGBoost SHAP

Hourly electricity demand forecasting on real EIA grid data (Texas, 2018–2023).

• Time-series feature engineering: lag features, rolling stats, cyclical encoding
• XGBoost achieving 2.40% MAPE — 48% improvement over seasonal naive baseline
• Weather integration via Open-Meteo API
• Python XGBoost Scikit-learn Pandas

Probabilistic match outcome modeling with explicit focus on draw modeling.

• Calibrated Home / Draw / Away probabilities using Platt Scaling
• Expected Points (xPts) league table from match-level probabilities
• 10,000 Monte Carlo season simulations for title, top-4, and relegation probabilities
• Python XGBoost Monte Carlo Simulation