Cross River

Data Scientist - AML

Cross River

full-time

Posted on:

Origin:  • 🇮🇱 Israel

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Job Level

Mid-LevelSenior

Tech Stack

PythonSQL

About the role

  • Play an essential role in driving data-informed decisions that strengthen our AML/CFT compliance and empower business leaders with actionable insights
  • Transform complex data into clear, compelling analysis to support strategic recommendations and influence compliance outcomes and operational success
  • Collaborate with cross-functional partners to deliver comprehensive view of risk and monitor impact of implemented strategies
  • Design and refine transaction monitoring scenarios; produce high-quality dashboards and reports guiding optimization efforts
  • Measure and monitor results of applied recommendations and present adjustments; provide routine and ad hoc reporting tied to AML/CFT compliance
  • Support development of policies and procedures for AML transaction monitoring life cycle including reviews of scenario validation, segmentation and optimization tools

Requirements

  • 3+ years or relevant experience in the data analytics and/or data science field - Must
  • 3+ years' experience using SQL/Python for querying data and for data manipulation / transformation – Must
  • Significant experience in implementing an end-to-end AML/CFT compliance management platform that includes transaction monitoring tools - Must
  • Native level English both written and verbal, sufficient for achieving consensus and success in a remote and largely asynchronous work environment and good Hebrew
  • Experience implementing AML or Fraud Transaction Monitoring in Palantir - A plus
  • Experience in Business Intelligence/Data Exploration and Visualization tools
  • Experience in Implementing AML or Fraud Solutions, AML/Fraud Alert Generation Process, Scenario Management
  • Strong analytic skills and problem solving with the ability to extract, collect, organize, analyze and interpret trends or patterns in complex data sets
  • Knowledge and understanding of machine learning and artificial intelligence approaches, ideally with experience of applying them in an AML/Fraud context