Data Scientist – Fraud AML

Morristown, NJ - USA

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

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 supporttriage 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

Required Skills:

Database SQL Python DataRobot Snowflake GitHub

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 de...