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)

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