Description

Build and maintain production ML solutions in financial services, focusing on AML compliance, fraud detection, and core FS use cases.

This role is on-site.

Responsibilities

• Retrain and calibrate governed predictive models to improve precision, recall, and reduce false positives.
• Design, deploy, monitor, and maintain models using deep learning, tree-based, reinforcement learning, clustering, time series, causal methods, and NLP.
• Define thresholds and trigger retraining based on continuous monitoring of model performance, drift, and stability.
• Partner with ML Engineering to provide L2/L3 production support, triaging incidents and performing root-cause analysis.
• Deliver customer segmentation, anomaly detection, and forecasting solutions as production-ready models.

Required Skills

• 12+ years of relevant work experience in fintech fraud risk.
• Deep understanding of money movement products, banking, lending, and fraud detection data.
• Proficiency in Python and SQL for writing production-quality code for data science workflows.
• Expertise in designing and building efficient and reusable data pipelines and frameworks for machine learning models.
• Experience leveraging ML frameworks including deep learning, tree-based, reinforcement learning, clustering, time series, causal methods, and NLP.
• Familiarity with MLOps practices for model deployment and monitoring.
• Strong business problem-solving, communication, and collaboration skills.
• Ability to develop a deep statistical understanding of large, complex datasets.

Preferred Skills

• Bachelor's degree in a quantitative field.

Key Skills

Education

Bachelor's degree