Data Scientist (Fraud Modeling Lead)

Job Title: Data Scientist (Fraud Modeling Lead)

Location: Remote (Federal Project)
Duration: Contract

🔷 Job Description:

We are seeking an experienced Data Scientist (Fraud Modeling Lead) to design and implement advanced fraud detection and risk modeling solutions. The ideal candidate will have strong expertise in machine learning, anomaly detection, and model deployment, with experience working in regulated environments.

This role will focus on building scalable fraud models, implementing human-in-the-loop (HITL) learning systems, and driving analytics to improve decision-making and reduce risk.

🔷 Key Responsibilities:

• Design and develop fraud risk models using supervised learning and anomaly detection techniques to identify suspicious entities and events.
• Perform feature engineering, model training, validation, and optimization for high-performance fraud detection systems.
• Build and implement reinforcement learning / active learning loops with human feedback to improve model accuracy and decision thresholds.
• Develop and maintain model documentation, including assumptions, limitations, model cards, and evaluation reports.
• Define and track key performance metrics such as precision, recall, F1-score, false positive rate, and model drift.
• Conduct bias and drift analysis to ensure fairness, compliance, and model stability.
• Collaborate with engineering teams to deploy models into production environments using MLOps best practices.
• Work closely with SMEs and stakeholders to align models with business risk and fraud prevention strategies.