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Compliance Data Scientist IRB FCC CDI Bucarest, Roumanie

Societe Generale

Societe Generale

Legal, Data Science
Romania
Posted on Sunday, June 9, 2024
Compliance/ International Retail Banking/ Financial Crime (CPLE/IRB/FCC) is responsible for facilitating and monitoring, in all entities of the International Retail Banking scope, the compliance with regulations applicable to the fight against financial crime: know-your-customer (KYC) obligations, fight against money laundering and terrorist financing, and compliance with international embargoes and sanctions. The Compliance Data Scientist is in charge of calibration of Financial Crime Scenarios and management of scenarios in production, whose function mainly consists of ensuring the optimization of the Group’s framework for fighting financial crime through its Transactions Monitoring Systems, by ensuring proper and correct calibrations of associated scenarios. Main tasks and responsibilities:
  • Calibration of Financial Crime Compliance Scenarios, employing statistical analysis and Group’s models;
  • Annual review of scenarios;
  • Data analysis: extraction of data, cleaning, analyzing, visualizing in order to support conclusions and support decision-making;
  • Perform ad-hoc studies relevant for the optimization of financial security scenarios, the number of generated alerts;
  • Developing custom data models and algorithms to apply to data sets; Identifying patterns and trends in data sets to discover new business perspective; Develops predictive and statistical models to proactively spot problems and business opportunities;
  • It uses models and machine learning methods to improve the quality of the data and implicitly the functionality of the product; deliver data-driven suggestions and recommendations to the executive team.
  • Analyzes the pertinence of generated alerts and proposes new thresholds ensuring operational efficiency of scenarios, as well as the quality of generated alerts;
  • Testing of developments in UAT; Validation of developments for production;
  • Recurrent data mining tuning thanks to keyword data analysis using Python technology, and weekly review of data research results on banking transactions;
  • PCT controls: Consistency between validated scenarios and scenarios in production; Consistency of parameters & thresholds between validated scenarios and scenarios in production.