Credit Tier Prediction ML Pipeline

Role: Volunteer Data Scientist — Microsoft Data Science Team · Period: Summer 2025

Overview

Built and shipped an end-to-end machine learning pipeline for multi-class credit tier prediction over 4 million+ financial records, in a volunteer engagement with the Microsoft Data Science team. The work fed directly into customer segmentation insights used to inform credit acceptance decisions.

What I did

• End-to-end pipeline from raw financial records to deployed prediction outputs, including ingestion, validation, feature engineering, model training, and inference scoring.
• Ensemble modeling and feature engineering that lifted overall accuracy by 4% and cut false positives by 6% versus the baseline — a meaningful difference at this scale, where each percentage point translates to thousands of customers correctly tiered.
• Large-scale hyperparameter tuning and benchmarking across model families, with rigorous cross-validation to ensure the gains generalized beyond the training distribution.
• Translated model outputs into actionable customer segmentation insights that informed downstream credit acceptance decisions, with attention to data governance and compliance constraints.

Impact

The pipeline reduced false-positive credit-tier assignments while improving overall accuracy, which directly supports better lending decisions — fewer customers misclassified into the wrong tier means more accurate offers, fewer manual reviews, and stronger compliance posture.

Tech stack

Python · pandas · scikit-learn · ensemble methods (Gradient Boosting, Random Forest) · feature engineering · hyperparameter tuning · cross-validation