Data Scientist developing advanced data analytics solutions for financial crime detection at Morgan Stanley. Collaborating with cross-functional teams to implement analytics solutions in a hybrid work environment.
Responsibilities
Develop and implement advanced data analytics solutions for financial crimes detection and prevention.
Use graph-based techniques, graph analytics infrastructure to identify client behavior patterns to assist in financial crime investigations and build solutions for financial crime detection.
Perform statistical and quantitative analysis to support inclusion of risk attributes, analyze, and propose thresholds for financial crime detection controls.
Conduct research and development efforts to identify novel methods to deliver efficiency and effectiveness across analytical solutions.
Collaborate closely with stakeholders in the Global Financial Crimes, Technology and Model Risk Management departments to deliver analytics solutions from conception to deployment.
Requirements
Advanced quantitative degree in machine learning, statistics, mathematics, data science, computer science, engineering, or other highly quantitative fields.
3+ years of hands-on industry experience in conducting statistical analysis, developing quantitative models and deploying production quality code adhering to best practices and SDLC standards.
Experience with graph databases and network analysis tools (e.g. NetworkX, Neo4J)
Experience using PySpark in distributed environments like Hadoop and cloud
In-depth knowledge of financial crime typologies, BSA/AML regulations and experience with financial crime specific platforms (e.g. Actimize, Quantexa)
Strong presentation, written and verbal communication skills, with the ability to articulate complex technical concepts to non-technical audiences.
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