Data Scientist – Fraud​/AML

Purple Drive

Data Scientist – Fraud / AML Share Morristown, NJ

Overview:

Key responsibilities:

Retrain and calibrate governed predictive models for AML compliance, fraud, and core FS use cases to improve precision/recall and reduce false positives. Relevant work experience in fintech fraud risk, with deep understanding of money movement products, banking, lending, and fraud detection data

Deliver customer segmentation, anomaly detection, and forecasting solutions by applying hands-on ML expertise to ship production-ready models in financial services. Leverage experience across credit risk and fraud to design, deploy, monitor, and maintain models (deep learning, tree-based, reinforcement learning, clustering, time series, causal methods, and NLP) with a deep understanding of payment systems, money movement, banking, and lending.

Continuously monitor model performance, drift, and stability; define thresholds and trigger retraining with clear acceptance criteria.

Partner with ML Engineering to provide L2/L3 production support—triage incidents, perform root-cause analysis, and implement hotfixes within MLOps guardrails.

Engineer features and prepare training/eval datasets with Data Engineering; contribute to the design and rollout of a reusable feature store.

Document models, assumptions, and controls; perform structured handoffs to MLOps/ML Engineering for compliant deployment.

Write production-quality code for DS workflows (SQL, Python); use advanced Excel for analysis/reporting; enforce testing and reproducibility.

Ability to quickly develop a deep statistical understanding of large, complex datasets

Expertise in designing and building efficient and reusable data pipelines and framework for machine learning models

Strong business problem solving, communication and collaboration skills

Required Experience:

6 - 8 Years of Experience