Data Scientist - AML Analytics & Modeling
Core
Build and deploy machine learning models to detect suspicious patterns, risk indicators, and potential business outcomes in customer and transaction data for anti-money-laundering purposes.
Role type
Senior IC data scientist (AML analytics & modeling)
Builds
Production ML models and data-driven solutions for fraud and AML detection
Domain
Financial services / Anti-money laundering
Deliverable
production ML models
Required skills
Python, PySpark, SQL, statistical methods, machine learning algorithms, model validation, data mining, cloud platforms (AWS/Azure/GCP)
Preferred skills
Banking/financial services experience, fraud/AML modeling experience, neural networks, cloud platforms (AWS/Azure/GCP)
Technologies
AWS, Azure, Google Cloud, Python, PySpark, SQL, PyTorch, TensorFlow
Responsibilities
Analyze large volumes of structured and unstructured data to produce actionable business insights; Gather, process, mine, and analyze customer, account, and transaction data from varied sources; Create datasets and samples, build statistical and machine-learning models, and monitor, document, and report on models; Apply methods such as logistic regression, clustering, gradient boosting, neural networks, and other machine-learning algorithms to identify suspicious patterns, risk indicators, and potential business outcomes; Run analytical experiments to identify opportunities to improve products, processes, and existing strategies, and recommend new approaches; Communicate findings and progress on modeling initiatives to senior executives and business partners, using appropriate visualizations and data storytelling; Lead innovative anti-money-laundering analytics projects and help develop data-driven solutions.
Seniority
Senior, hands-on IC
