Credit Risk Data Scientist (Remote) – W2 Role Only , NO C2C

REMOTE, Posted 9 hours ago We are looking for an experienced Credit Risk Data Scientist to join our team on a contract basis. The ideal candidate will design, build, and evaluate machine learning models to predict credit risk for various short-term lending products (e.g., tax refund advances, Buy Now Pay Later (BNPL), installment loans). Contract Work from home not available Travel not required Job Description We are looking for an experienced Credit Risk Data Scientist to join our team on a contract basis. The ideal candidate will design, build, and evaluate machine learning models to predict credit risk for various short-term lending products (e.g., tax refund advances, Buy Now Pay Later (BNPL), installment loans). Location: CA (Remote) Duration: 6 months Submission Slots: 3 Responsibilities: Build and evaluate machine learning models to predict credit risk for short-term lending products (e.g., BNPL, tax refund advances). Collaborate with product and risk teams to align models with business goals. Develop reusable data pipelines for feature generation, scoring, and reporting using Python, SQL, and CK Machine Learning. Deploy models in production and monitor performance (AUC, KS, Gini, PSI, CSI). Ensure model fairness, interpretability, and regulatory compliance (FCRA, ECOA). Degree in Math, Stats, Computer Science, or related field. 2+ years in Data Science, Machine Learning, Python, and SQL. Strong with scikit-learn, XGBoost, LightGBM, pandas, and numpy. Experience with credit risk modeling, including PD calibration, reject inference, and risk segmentation. Familiarity with tax/credit bureau data (TransUnion, Experian, Equifax) and alternative data (e.g., cash flow). To apply for this job email your details to alisha@luzontech.com Position Id : 1282 Originally Posted : Posted 9 hours ago We are looking for an experienced Credit Risk Data Scientist to join our team on a contract basis. The ideal candidate will design, build, and evaluate machine learning models to predict credit risk for various short-term lending products (e.g., tax refund advances, Buy Now Pay Later (BNPL), installment loans).