Data Scientist - Fraud / AML
- 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
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