Machine Learning Engineer
Current- 2018.1 - present
- Developed anti-fraud rules with rule-extraction from CART/ Regularized Random Forest. Achieved a decrease of 20% in transaction disturb rate while keeping same level of recall rate of fraudulent transaction
- Feature extraction and derivation from trading behavior, LBS, devices and other data sources to support customer marketing and application scorecard development (XGboost) for personal loan product
- Understood offline collection business scenario, analyzed user behavior and built customer segmentation model with K-means++ to help differentiated marketing
- Summarized the analytical needs from various departments, conducted data cleansing/manipulation/feature extraction and designed customer label system2017.1-2017.12
- P2P merchants risk modeling using Random Forest + SMOTE + Leave-One-Out to overcome sample imbalance