ML Engineer - Fraud Risk/AI Data Science

Title: ML Engineer - Fraud Risk/AI Data Science Job Type: Contract Contract Length: 4-5 months Pay Range: 80-85/hr Target Start Date: 3/11 Location: Remote About The Opportunity Our client, a leader in FinTech, is looking for a skilled ML Engineer - Fraud Risk/AI Data Science to join their team for a 4-5 month engagement. This project involves designing, building, and deploying machine learning models to predict fraud risk in a real-time environment, and building efficient, reusable data pipelines. This is a high-impact role that requires a self-motivated professional who can hit the ground running and deliver results quickly. Key Responsibilities & Deliverables This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include: • Assist in development, validation, and maintenance of real-time features. • Design, build, evaluate, and defend machine learning models to predict fraud risk. • Build efficient and reusable data pipelines for feature generation, model development, scoring, and reporting using Python and SQL. • Deploy models in a production environment in collaboration with other data scientists and engineering teams. • Collaborate with business partners to create policies utilizing model results. • Implement metrics like AUC, KS, and Gini to monitor/measure model performance, and PSI/CSI to measure stability indices. • Ensure model fairness, interpretability, and compliance with FCRA, ECOA, and other relevant regulatory frameworks. Required Skills & Experience: We are looking for someone with a proven track record of successful contract engagements. The ideal candidate will have: • 2+ years of industrial experience in Data Science, Machine Learning, and related areas. • A Degree in Mathematics, Statistics, Computer Science, or a related field. • Deep expertise in: • Python and SQL; Strong proficiency in Python with libraries such as scikit-learn, XGBoost, LightGBM, pandas, and numpy. • A variety of machine learning techniques, including tree-based models, regression models, time series, causal analysis, and clustering. • Credit risk modeling concepts, including PD calibration, reject inference, adverse action logic, and risk segmentation, preferably in a credit risk/lending or FinTech domain. • Demonstrated ability to work autonomously and manage your own time effectively to meet project goals. • Experience with tax and/or credit bureau data in credit model development. • Familiarity with cash flow data as alternative or complementary data sources. • Strong business problem solving, communication, and collaboration skills. W2 only (No C2C or 1099 contractors)